id	sid	tid	token	lemma	pos
fcis-31893	1	1	frontiers	frontier	NOUN
fcis-31893	1	2	in	in	ADP
fcis-31893	1	3	computing	computing	NOUN
fcis-31893	1	4	and	and	CCONJ
fcis-31893	1	5	intelligent	intelligent	ADJ
fcis-31893	1	6	systems	system	NOUN
fcis-31893	1	7	issn	issn	VERB
fcis-31893	1	8	:	:	PUNCT
fcis-31893	1	9	2832	2832	NUM
fcis-31893	1	10	-	-	SYM
fcis-31893	1	11	6024	6024	NUM
fcis-31893	1	12	|	|	NOUN
fcis-31893	1	13	vol	vol	NOUN
fcis-31893	1	14	.	.	PROPN
fcis-31893	2	1	13	13	NUM
fcis-31893	2	2	,	,	PUNCT
fcis-31893	2	3	no	no	INTJ
fcis-31893	2	4	.	.	NOUN
fcis-31893	2	5	3	3	NUM
fcis-31893	2	6	,	,	PUNCT
fcis-31893	2	7	2025	2025	NUM
fcis-31893	2	8	39	39	NUM
fcis-31893	2	9	research	research	NOUN
fcis-31893	2	10	on	on	ADP
fcis-31893	2	11	urban	urban	ADJ
fcis-31893	2	12	sound	sound	ADJ
fcis-31893	2	13	classification	classification	NOUN
fcis-31893	2	14	based	base	VERB
fcis-31893	2	15	on	on	ADP
fcis-31893	2	16	convnext‐feca	convnext‐feca	NOUN
fcis-31893	2	17	model	model	NOUN
fcis-31893	2	18	tianxiang	tianxiang	PROPN
fcis-31893	2	19	zhu	zhu	PROPN
fcis-31893	2	20	1	1	NUM
fcis-31893	2	21	,	,	PUNCT
fcis-31893	2	22	2	2	NUM
fcis-31893	2	23	1	1	NUM
fcis-31893	2	24	school	school	NOUN
fcis-31893	2	25	of	of	ADP
fcis-31893	2	26	information	information	NOUN
fcis-31893	2	27	and	and	CCONJ
fcis-31893	2	28	control	control	PROPN
fcis-31893	2	29	engineering	engineering	PROPN
fcis-31893	2	30	,	,	PUNCT
fcis-31893	2	31	jilin	jilin	PROPN
fcis-31893	2	32	institute	institute	PROPN
fcis-31893	2	33	of	of	ADP
fcis-31893	2	34	chemical	chemical	PROPN
fcis-31893	2	35	technology	technology	PROPN
fcis-31893	2	36	,	,	PUNCT
fcis-31893	2	37	jilin	jilin	PROPN
fcis-31893	2	38	,	,	PUNCT
fcis-31893	2	39	jilin	jilin	PROPN
fcis-31893	2	40	132000	132000	NUM
fcis-31893	2	41	,	,	PUNCT
fcis-31893	2	42	china	china	PROPN
fcis-31893	2	43	2	2	NUM
fcis-31893	2	44	school	school	NOUN
fcis-31893	2	45	of	of	ADP
fcis-31893	2	46	computer	computer	NOUN
fcis-31893	2	47	and	and	CCONJ
fcis-31893	2	48	information	information	NOUN
fcis-31893	2	49	,	,	PUNCT
fcis-31893	2	50	dezhou	dezhou	PROPN
fcis-31893	2	51	university	university	PROPN
fcis-31893	2	52	,	,	PUNCT
fcis-31893	2	53	dezhou	dezhou	PROPN
fcis-31893	2	54	shandong	shandong	PROPN
fcis-31893	2	55	,	,	PUNCT
fcis-31893	2	56	253000	253000	NUM
fcis-31893	2	57	,	,	PUNCT
fcis-31893	2	58	china	china	PROPN
fcis-31893	2	59	abstract	abstract	NOUN
fcis-31893	2	60	:	:	PUNCT
fcis-31893	2	61	with	with	ADP
fcis-31893	2	62	the	the	DET
fcis-31893	2	63	increasing	increase	VERB
fcis-31893	2	64	severity	severity	NOUN
fcis-31893	2	65	of	of	ADP
fcis-31893	2	66	urban	urban	ADJ
fcis-31893	2	67	sound	sound	NOUN
fcis-31893	2	68	pollution	pollution	NOUN
fcis-31893	2	69	,	,	PUNCT
fcis-31893	2	70	efficient	efficient	ADJ
fcis-31893	2	71	and	and	CCONJ
fcis-31893	2	72	accurate	accurate	ADJ
fcis-31893	2	73	urban	urban	ADJ
fcis-31893	2	74	sound	sound	ADJ
fcis-31893	2	75	classification	classification	NOUN
fcis-31893	2	76	and	and	CCONJ
fcis-31893	2	77	recognition	recognition	NOUN
fcis-31893	2	78	has	have	AUX
fcis-31893	2	79	become	become	VERB
fcis-31893	2	80	an	an	DET
fcis-31893	2	81	important	important	ADJ
fcis-31893	2	82	topic	topic	NOUN
fcis-31893	2	83	in	in	ADP
fcis-31893	2	84	the	the	DET
fcis-31893	2	85	field	field	NOUN
fcis-31893	2	86	of	of	ADP
fcis-31893	2	87	urban	urban	ADJ
fcis-31893	2	88	environmental	environmental	ADJ
fcis-31893	2	89	monitoring	monitoring	NOUN
fcis-31893	2	90	.	.	PUNCT
fcis-31893	3	1	in	in	ADP
fcis-31893	3	2	this	this	DET
fcis-31893	3	3	paper	paper	NOUN
fcis-31893	3	4	,	,	PUNCT
fcis-31893	3	5	we	we	PRON
fcis-31893	3	6	propose	propose	VERB
fcis-31893	3	7	an	an	DET
fcis-31893	3	8	urban	urban	ADJ
fcis-31893	3	9	noise	noise	NOUN
fcis-31893	3	10	classification	classification	NOUN
fcis-31893	3	11	method	method	NOUN
fcis-31893	3	12	based	base	VERB
fcis-31893	3	13	on	on	ADP
fcis-31893	3	14	the	the	DET
fcis-31893	3	15	frequency	frequency	NOUN
fcis-31893	3	16	enhanced	enhance	VERB
fcis-31893	3	17	convolution	convolution	NOUN
fcis-31893	3	18	attention	attention	NOUN
fcis-31893	3	19	(	(	PUNCT
fcis-31893	3	20	convnext	convnext	NOUN
fcis-31893	3	21	-	-	PUNCT
fcis-31893	3	22	feca	feca	NOUN
fcis-31893	3	23	)	)	PUNCT
fcis-31893	3	24	model	model	NOUN
fcis-31893	3	25	.	.	PUNCT
fcis-31893	4	1	by	by	ADP
fcis-31893	4	2	fusing	fuse	VERB
fcis-31893	4	3	the	the	DET
fcis-31893	4	4	spectrogram	spectrogram	NOUN
fcis-31893	4	5	and	and	CCONJ
fcis-31893	4	6	mfcc	mfcc	NOUN
fcis-31893	4	7	features	feature	VERB
fcis-31893	4	8	in	in	ADP
fcis-31893	4	9	the	the	DET
fcis-31893	4	10	early	early	ADJ
fcis-31893	4	11	stage	stage	NOUN
fcis-31893	4	12	,	,	PUNCT
fcis-31893	4	13	the	the	DET
fcis-31893	4	14	method	method	NOUN
fcis-31893	4	15	makes	make	VERB
fcis-31893	4	16	full	full	ADJ
fcis-31893	4	17	use	use	NOUN
fcis-31893	4	18	of	of	ADP
fcis-31893	4	19	the	the	DET
fcis-31893	4	20	advantages	advantage	NOUN
fcis-31893	4	21	of	of	ADP
fcis-31893	4	22	the	the	DET
fcis-31893	4	23	two	two	NUM
fcis-31893	4	24	features	feature	NOUN
fcis-31893	4	25	,	,	PUNCT
fcis-31893	4	26	and	and	CCONJ
fcis-31893	4	27	introduces	introduce	VERB
fcis-31893	4	28	the	the	DET
fcis-31893	4	29	frequency	frequency	NOUN
fcis-31893	4	30	enhanced	enhance	VERB
fcis-31893	4	31	convolutional	convolutional	ADJ
fcis-31893	4	32	attention	attention	NOUN
fcis-31893	4	33	mechanism	mechanism	NOUN
fcis-31893	4	34	(	(	PUNCT
fcis-31893	4	35	feca	feca	PROPN
fcis-31893	4	36	)	)	PUNCT
fcis-31893	4	37	to	to	PART
fcis-31893	4	38	adaptively	adaptively	ADV
fcis-31893	4	39	pay	pay	VERB
fcis-31893	4	40	attention	attention	NOUN
fcis-31893	4	41	to	to	ADP
fcis-31893	4	42	the	the	DET
fcis-31893	4	43	changes	change	NOUN
fcis-31893	4	44	in	in	ADP
fcis-31893	4	45	the	the	DET
fcis-31893	4	46	frequency	frequency	NOUN
fcis-31893	4	47	band	band	NOUN
fcis-31893	4	48	of	of	ADP
fcis-31893	4	49	the	the	DET
fcis-31893	4	50	audio	audio	ADJ
fcis-31893	4	51	signal	signal	NOUN
fcis-31893	4	52	,	,	PUNCT
fcis-31893	4	53	which	which	PRON
fcis-31893	4	54	effectively	effectively	ADV
fcis-31893	4	55	improves	improve	VERB
fcis-31893	4	56	the	the	DET
fcis-31893	4	57	classification	classification	NOUN
fcis-31893	4	58	performance	performance	NOUN
fcis-31893	4	59	.	.	PUNCT
fcis-31893	5	1	experimental	experimental	ADJ
fcis-31893	5	2	results	result	NOUN
fcis-31893	5	3	show	show	VERB
fcis-31893	5	4	that	that	SCONJ
fcis-31893	5	5	the	the	DET
fcis-31893	5	6	classification	classification	NOUN
fcis-31893	5	7	accuracy	accuracy	NOUN
fcis-31893	5	8	of	of	ADP
fcis-31893	5	9	the	the	DET
fcis-31893	5	10	convnext	convnext	ADJ
fcis-31893	5	11	-	-	PUNCT
fcis-31893	5	12	feca	feca	NOUN
fcis-31893	5	13	model	model	NOUN
fcis-31893	5	14	is	be	AUX
fcis-31893	5	15	98.5	98.5	NUM
fcis-31893	5	16	%	%	NOUN
fcis-31893	5	17	on	on	ADP
fcis-31893	5	18	the	the	DET
fcis-31893	5	19	urban	urban	ADJ
fcis-31893	5	20	sound8k	sound8k	NOUN
fcis-31893	5	21	dataset	dataset	NOUN
fcis-31893	5	22	,	,	PUNCT
fcis-31893	5	23	showing	show	VERB
fcis-31893	5	24	strong	strong	ADJ
fcis-31893	5	25	robustness	robustness	NOUN
fcis-31893	5	26	and	and	CCONJ
fcis-31893	5	27	generalization	generalization	NOUN
fcis-31893	5	28	ability	ability	NOUN
fcis-31893	5	29	.	.	PUNCT
fcis-31893	6	1	keywords	keyword	NOUN
fcis-31893	6	2	:	:	PUNCT
fcis-31893	6	3	sound	sound	VERB
fcis-31893	6	4	classification	classification	NOUN
fcis-31893	6	5	;	;	PUNCT
fcis-31893	6	6	convnext	convnext	NOUN
fcis-31893	6	7	;	;	PUNCT
fcis-31893	6	8	frequency	frequency	NOUN
fcis-31893	6	9	-	-	PUNCT
fcis-31893	6	10	enhanced	enhance	VERB
fcis-31893	6	11	convolution	convolution	NOUN
fcis-31893	6	12	attention	attention	NOUN
fcis-31893	6	13	;	;	PUNCT
fcis-31893	6	14	spectrogram	spectrogram	NOUN
fcis-31893	6	15	;	;	PUNCT
fcis-31893	6	16	mfcc	mfcc	NOUN
fcis-31893	6	17	.	.	PUNCT
fcis-31893	7	1	1	1	X
fcis-31893	7	2	.	.	X
fcis-31893	7	3	introduction	introduction	NOUN
fcis-31893	7	4	in	in	ADP
fcis-31893	7	5	recent	recent	ADJ
fcis-31893	7	6	years	year	NOUN
fcis-31893	7	7	,	,	PUNCT
fcis-31893	7	8	deep	deep	ADJ
fcis-31893	7	9	learning	learning	NOUN
fcis-31893	7	10	technologies	technology	NOUN
fcis-31893	7	11	have	have	AUX
fcis-31893	7	12	been	be	AUX
fcis-31893	7	13	widely	widely	ADV
fcis-31893	7	14	applied	apply	VERB
fcis-31893	7	15	in	in	ADP
fcis-31893	7	16	urban	urban	ADJ
fcis-31893	7	17	environmental	environmental	ADJ
fcis-31893	7	18	noise	noise	NOUN
fcis-31893	7	19	monitoring	monitoring	NOUN
fcis-31893	7	20	,	,	PUNCT
fcis-31893	7	21	gradually	gradually	ADV
fcis-31893	7	22	replacing	replace	VERB
fcis-31893	7	23	traditional	traditional	ADJ
fcis-31893	7	24	manual	manual	ADJ
fcis-31893	7	25	sampling	sampling	NOUN
fcis-31893	7	26	and	and	CCONJ
fcis-31893	7	27	analysis	analysis	NOUN
fcis-31893	7	28	methods	method	NOUN
fcis-31893	7	29	.	.	PUNCT
fcis-31893	8	1	for	for	ADP
fcis-31893	8	2	example	example	NOUN
fcis-31893	8	3	,	,	PUNCT
fcis-31893	8	4	audio	audio	ADJ
fcis-31893	8	5	classification	classification	NOUN
fcis-31893	8	6	methods	method	NOUN
fcis-31893	8	7	based	base	VERB
fcis-31893	8	8	on	on	ADP
fcis-31893	8	9	convolutional	convolutional	ADJ
fcis-31893	8	10	neural	neural	ADJ
fcis-31893	8	11	networks	network	NOUN
fcis-31893	8	12	(	(	PUNCT
fcis-31893	8	13	cnn	cnn	PROPN
fcis-31893	8	14	)	)	PUNCT
fcis-31893	8	15	have	have	AUX
fcis-31893	8	16	performed	perform	VERB
fcis-31893	8	17	well	well	ADV
fcis-31893	8	18	on	on	ADP
fcis-31893	8	19	datasets	dataset	NOUN
fcis-31893	8	20	such	such	ADJ
fcis-31893	8	21	as	as	ADP
fcis-31893	8	22	urbansound8k[1	urbansound8k[1	NOUN
fcis-31893	8	23	]	]	PUNCT
fcis-31893	8	24	.	.	PUNCT
fcis-31893	9	1	however	however	ADV
fcis-31893	9	2	,	,	PUNCT
fcis-31893	9	3	existing	exist	VERB
fcis-31893	9	4	methods	method	NOUN
fcis-31893	9	5	still	still	ADV
fcis-31893	9	6	show	show	VERB
fcis-31893	9	7	insufficient	insufficient	ADJ
fcis-31893	9	8	classification	classification	NOUN
fcis-31893	9	9	ability	ability	NOUN
fcis-31893	9	10	in	in	ADP
fcis-31893	9	11	complex	complex	ADJ
fcis-31893	9	12	noise	noise	NOUN
fcis-31893	9	13	environments	environment	NOUN
fcis-31893	9	14	,	,	PUNCT
fcis-31893	9	15	primarily	primarily	ADV
fcis-31893	9	16	due	due	ADP
fcis-31893	9	17	to	to	ADP
fcis-31893	9	18	limited	limited	ADJ
fcis-31893	9	19	extraction	extraction	NOUN
fcis-31893	9	20	of	of	ADP
fcis-31893	9	21	frequency	frequency	NOUN
fcis-31893	9	22	domain	domain	NOUN
fcis-31893	9	23	features	feature	NOUN
fcis-31893	9	24	.	.	PUNCT
fcis-31893	10	1	toaddress	toaddress	VERB
fcis-31893	10	2	this	this	DET
fcis-31893	10	3	issue	issue	NOUN
fcis-31893	10	4	,	,	PUNCT
fcis-31893	10	5	this	this	DET
fcis-31893	10	6	paper	paper	NOUN
fcis-31893	10	7	proposes	propose	VERB
fcis-31893	10	8	an	an	DET
fcis-31893	10	9	urban	urban	ADJ
fcis-31893	10	10	noise	noise	NOUN
fcis-31893	10	11	classification	classification	NOUN
fcis-31893	10	12	method	method	NOUN
fcis-31893	10	13	based	base	VERB
fcis-31893	10	14	on	on	ADP
fcis-31893	10	15	convnext	convnext	NOUN
fcis-31893	10	16	-	-	PUNCT
fcis-31893	10	17	feca	feca	NOUN
fcis-31893	10	18	.	.	PUNCT
fcis-31893	11	1	compared	compare	VERB
fcis-31893	11	2	to	to	ADP
fcis-31893	11	3	traditional	traditional	ADJ
fcis-31893	11	4	convolutional	convolutional	ADJ
fcis-31893	11	5	neural	neural	ADJ
fcis-31893	11	6	networks	network	NOUN
fcis-31893	11	7	,	,	PUNCT
fcis-31893	11	8	convnext[2	convnext[2	PROPN
fcis-31893	11	9	]	]	PUNCT
fcis-31893	11	10	has	have	VERB
fcis-31893	11	11	a	a	DET
fcis-31893	11	12	more	more	ADV
fcis-31893	11	13	efficient	efficient	ADJ
fcis-31893	11	14	feature	feature	NOUN
fcis-31893	11	15	extraction	extraction	NOUN
fcis-31893	11	16	capability	capability	NOUN
fcis-31893	11	17	,	,	PUNCT
fcis-31893	11	18	especially	especially	ADV
fcis-31893	11	19	suitable	suitable	ADJ
fcis-31893	11	20	for	for	ADP
fcis-31893	11	21	audio	audio	ADJ
fcis-31893	11	22	data	datum	NOUN
fcis-31893	11	23	processing	processing	NOUN
fcis-31893	11	24	.	.	PUNCT
fcis-31893	12	1	innovatively	innovatively	ADV
fcis-31893	12	2	,	,	PUNCT
fcis-31893	12	3	it	it	PRON
fcis-31893	12	4	introduces	introduce	VERB
fcis-31893	12	5	a	a	DET
fcis-31893	12	6	frequency	frequency	NOUN
fcis-31893	12	7	-	-	PUNCT
fcis-31893	12	8	enhanced	enhance	VERB
fcis-31893	12	9	convolutional	convolutional	ADJ
fcis-31893	12	10	attention	attention	NOUN
fcis-31893	12	11	mechanism	mechanism	NOUN
fcis-31893	12	12	(	(	PUNCT
fcis-31893	12	13	feca)[3	feca)[3	X
fcis-31893	12	14	]	]	PUNCT
fcis-31893	12	15	,	,	PUNCT
fcis-31893	12	16	which	which	PRON
fcis-31893	12	17	can	can	AUX
fcis-31893	12	18	adaptively	adaptively	ADV
fcis-31893	12	19	capture	capture	VERB
fcis-31893	12	20	changes	change	NOUN
fcis-31893	12	21	in	in	ADP
fcis-31893	12	22	frequency	frequency	NOUN
fcis-31893	12	23	components	component	NOUN
fcis-31893	12	24	of	of	ADP
fcis-31893	12	25	audio	audio	ADJ
fcis-31893	12	26	signals	signal	NOUN
fcis-31893	12	27	,	,	PUNCT
fcis-31893	12	28	significantly	significantly	ADV
fcis-31893	12	29	improving	improve	VERB
fcis-31893	12	30	classification	classification	NOUN
fcis-31893	12	31	accuracy	accuracy	NOUN
fcis-31893	12	32	and	and	CCONJ
fcis-31893	12	33	robustness	robustness	NOUN
fcis-31893	12	34	.	.	PUNCT
fcis-31893	13	1	1.1	1.1	NUM
fcis-31893	13	2	.	.	PUNCT
fcis-31893	13	3	data	datum	NOUN
fcis-31893	13	4	processing	processing	NOUN
fcis-31893	13	5	research	research	NOUN
fcis-31893	13	6	on	on	ADP
fcis-31893	13	7	urban	urban	ADJ
fcis-31893	13	8	noise	noise	NOUN
fcis-31893	13	9	classification	classification	NOUN
fcis-31893	13	10	and	and	CCONJ
fcis-31893	13	11	environmental	environmental	ADJ
fcis-31893	13	12	audio	audio	NOUN
fcis-31893	13	13	monitoring	monitoring	NOUN
fcis-31893	13	14	has	have	AUX
fcis-31893	13	15	made	make	VERB
fcis-31893	13	16	significant	significant	ADJ
fcis-31893	13	17	progress	progress	NOUN
fcis-31893	13	18	.	.	PUNCT
fcis-31893	14	1	with	with	ADP
fcis-31893	14	2	the	the	DET
fcis-31893	14	3	continuous	continuous	ADJ
fcis-31893	14	4	development	development	NOUN
fcis-31893	14	5	of	of	ADP
fcis-31893	14	6	deep	deep	ADJ
fcis-31893	14	7	learning	learning	NOUN
fcis-31893	14	8	technology	technology	NOUN
fcis-31893	14	9	,	,	PUNCT
fcis-31893	14	10	more	more	ADJ
fcis-31893	14	11	and	and	CCONJ
fcis-31893	14	12	more	more	ADJ
fcis-31893	14	13	studies	study	NOUN
fcis-31893	14	14	are	be	AUX
fcis-31893	14	15	beginning	begin	VERB
fcis-31893	14	16	to	to	PART
fcis-31893	14	17	utilize	utilize	VERB
fcis-31893	14	18	deep	deep	ADJ
fcis-31893	14	19	neural	neural	ADJ
fcis-31893	14	20	networks	network	NOUN
fcis-31893	14	21	for	for	ADP
fcis-31893	14	22	automatic	automatic	ADJ
fcis-31893	14	23	feature	feature	NOUN
fcis-31893	14	24	extraction	extraction	NOUN
fcis-31893	14	25	and	and	CCONJ
fcis-31893	14	26	classification	classification	NOUN
fcis-31893	14	27	of	of	ADP
fcis-31893	14	28	audio	audio	ADJ
fcis-31893	14	29	signals	signal	NOUN
fcis-31893	14	30	.	.	PUNCT
fcis-31893	15	1	however	however	ADV
fcis-31893	15	2	,	,	PUNCT
fcis-31893	15	3	the	the	DET
fcis-31893	15	4	noise	noise	NOUN
fcis-31893	15	5	classification	classification	NOUN
fcis-31893	15	6	task	task	NOUN
fcis-31893	15	7	still	still	ADV
fcis-31893	15	8	faces	face	VERB
fcis-31893	15	9	challenges	challenge	NOUN
fcis-31893	15	10	such	such	ADJ
fcis-31893	15	11	as	as	ADP
fcis-31893	15	12	diverse	diverse	ADJ
fcis-31893	15	13	noise	noise	NOUN
fcis-31893	15	14	sources	source	NOUN
fcis-31893	15	15	,	,	PUNCT
fcis-31893	15	16	complex	complex	ADJ
fcis-31893	15	17	environmental	environmental	ADJ
fcis-31893	15	18	backgrounds	background	NOUN
fcis-31893	15	19	,	,	PUNCT
fcis-31893	15	20	and	and	CCONJ
fcis-31893	15	21	insufficient	insufficient	ADJ
fcis-31893	15	22	frequency	frequency	NOUN
fcis-31893	15	23	feature	feature	NOUN
fcis-31893	15	24	extraction	extraction	NOUN
fcis-31893	15	25	.	.	PUNCT
fcis-31893	16	1	therefore	therefore	ADV
fcis-31893	16	2	,	,	PUNCT
fcis-31893	16	3	this	this	DET
fcis-31893	16	4	paper	paper	NOUN
fcis-31893	16	5	combines	combine	VERB
fcis-31893	16	6	early	early	ADJ
fcis-31893	16	7	fusion	fusion	NOUN
fcis-31893	16	8	feature	feature	NOUN
fcis-31893	16	9	extraction	extraction	NOUN
fcis-31893	16	10	methods	method	NOUN
fcis-31893	16	11	with	with	ADP
fcis-31893	16	12	the	the	DET
fcis-31893	16	13	convnext	convnext	ADJ
fcis-31893	16	14	-	-	PUNCT
fcis-31893	16	15	feca	feca	NOUN
fcis-31893	16	16	model	model	NOUN
fcis-31893	16	17	to	to	PART
fcis-31893	16	18	further	far	ADV
fcis-31893	16	19	improve	improve	VERB
fcis-31893	16	20	the	the	DET
fcis-31893	16	21	performance	performance	NOUN
fcis-31893	16	22	of	of	ADP
fcis-31893	16	23	audio	audio	ADJ
fcis-31893	16	24	classification	classification	NOUN
fcis-31893	16	25	.	.	PUNCT
fcis-31893	17	1	1.2	1.2	NUM
fcis-31893	17	2	.	.	PUNCT
fcis-31893	17	3	dataset	dataset	NOUN
fcis-31893	17	4	processing	process	VERB
fcis-31893	17	5	this	this	DET
fcis-31893	17	6	article	article	NOUN
fcis-31893	17	7	uses	use	VERB
fcis-31893	17	8	the	the	DET
fcis-31893	17	9	urbansound8k	urbansound8k	PROPN
fcis-31893	17	10	dataset	dataset	NOUN
fcis-31893	17	11	,	,	PUNCT
fcis-31893	17	12	which	which	PRON
fcis-31893	17	13	consists	consist	VERB
fcis-31893	17	14	of	of	ADP
fcis-31893	17	15	8,732	8,732	NUM
fcis-31893	17	16	audio	audio	ADJ
fcis-31893	17	17	clips	clip	NOUN
fcis-31893	17	18	categorized	categorize	VERB
fcis-31893	17	19	into	into	ADP
fcis-31893	17	20	10	10	NUM
fcis-31893	17	21	different	different	ADJ
fcis-31893	17	22	classes	class	NOUN
fcis-31893	17	23	.	.	PUNCT
fcis-31893	18	1	each	each	DET
fcis-31893	18	2	audio	audio	NOUN
fcis-31893	18	3	clip	clip	NOUN
fcis-31893	18	4	is	be	AUX
fcis-31893	18	5	approximately	approximately	ADV
fcis-31893	18	6	4	4	NUM
fcis-31893	18	7	seconds	second	NOUN
fcis-31893	18	8	long	long	ADV
fcis-31893	18	9	and	and	CCONJ
fcis-31893	18	10	stored	store	VERB
fcis-31893	18	11	in	in	ADP
fcis-31893	18	12	wav	wav	NOUN
fcis-31893	18	13	format	format	NOUN
fcis-31893	18	14	.	.	PUNCT
fcis-31893	19	1	the	the	DET
fcis-31893	19	2	dataset	dataset	NOUN
fcis-31893	19	3	primarily	primarily	ADV
fcis-31893	19	4	includes	include	VERB
fcis-31893	19	5	common	common	ADJ
fcis-31893	19	6	noises	noise	NOUN
fcis-31893	19	7	in	in	ADP
fcis-31893	19	8	urban	urban	ADJ
fcis-31893	19	9	environments	environment	NOUN
fcis-31893	19	10	,	,	PUNCT
fcis-31893	19	11	such	such	ADJ
fcis-31893	19	12	as	as	ADP
fcis-31893	19	13	traffic	traffic	NOUN
fcis-31893	19	14	noise	noise	NOUN
fcis-31893	19	15	,	,	PUNCT
fcis-31893	19	16	mechanical	mechanical	ADJ
fcis-31893	19	17	noise	noise	NOUN
fcis-31893	19	18	,	,	PUNCT
fcis-31893	19	19	sirens	siren	NOUN
fcis-31893	19	20	,	,	PUNCT
fcis-31893	19	21	and	and	CCONJ
fcis-31893	19	22	dog	dog	NOUN
fcis-31893	19	23	barks	bark	VERB
fcis-31893	19	24	.	.	PUNCT
fcis-31893	20	1	these	these	DET
fcis-31893	20	2	categories	category	NOUN
fcis-31893	20	3	represent	represent	VERB
fcis-31893	20	4	typical	typical	ADJ
fcis-31893	20	5	urban	urban	ADJ
fcis-31893	20	6	environmental	environmental	ADJ
fcis-31893	20	7	noise	noise	NOUN
fcis-31893	20	8	,	,	PUNCT
fcis-31893	20	9	and	and	CCONJ
fcis-31893	20	10	each	each	DET
fcis-31893	20	11	class	class	NOUN
fcis-31893	20	12	of	of	ADP
fcis-31893	20	13	audio	audio	NOUN
fcis-31893	20	14	samples	sample	NOUN
fcis-31893	20	15	comes	come	VERB
fcis-31893	20	16	from	from	ADP
fcis-31893	20	17	different	different	ADJ
fcis-31893	20	18	urban	urban	ADJ
fcis-31893	20	19	settings	setting	NOUN
fcis-31893	20	20	,	,	PUNCT
fcis-31893	20	21	offering	offer	VERB
fcis-31893	20	22	diversity	diversity	NOUN
fcis-31893	20	23	and	and	CCONJ
fcis-31893	20	24	challenges	challenge	NOUN
fcis-31893	20	25	,	,	PUNCT
fcis-31893	20	26	which	which	PRON
fcis-31893	20	27	can	can	AUX
fcis-31893	20	28	provide	provide	VERB
fcis-31893	20	29	researchers	researcher	NOUN
fcis-31893	20	30	with	with	ADP
fcis-31893	20	31	a	a	DET
fcis-31893	20	32	rich	rich	ADJ
fcis-31893	20	33	dataset	dataset	NOUN
fcis-31893	20	34	for	for	ADP
fcis-31893	20	35	sound	sound	ADJ
fcis-31893	20	36	classification	classification	NOUN
fcis-31893	20	37	tasks	task	NOUN
fcis-31893	20	38	.	.	PUNCT
fcis-31893	21	1	1.3	1.3	NUM
fcis-31893	21	2	.	.	PUNCT
fcis-31893	21	3	data	datum	NOUN
fcis-31893	21	4	preprocessing	preprocesse	VERB
fcis-31893	21	5	in	in	ADP
fcis-31893	21	6	order	order	NOUN
fcis-31893	21	7	to	to	PART
fcis-31893	21	8	improve	improve	VERB
fcis-31893	21	9	data	data	NOUN
fcis-31893	21	10	diversity	diversity	NOUN
fcis-31893	21	11	and	and	CCONJ
fcis-31893	21	12	enhance	enhance	VERB
fcis-31893	21	13	the	the	DET
fcis-31893	21	14	model	model	NOUN
fcis-31893	21	15	's	's	PART
fcis-31893	21	16	generalization	generalization	NOUN
fcis-31893	21	17	capability	capability	NOUN
fcis-31893	21	18	,	,	PUNCT
fcis-31893	21	19	this	this	DET
fcis-31893	21	20	paper	paper	NOUN
fcis-31893	21	21	employs	employ	VERB
fcis-31893	21	22	various	various	ADJ
fcis-31893	21	23	data	datum	NOUN
fcis-31893	21	24	augmentation	augmentation	NOUN
fcis-31893	21	25	techniques	technique	NOUN
fcis-31893	21	26	in	in	ADP
fcis-31893	21	27	audio	audio	ADJ
fcis-31893	21	28	data	datum	NOUN
fcis-31893	21	29	preprocessing	preprocessing	NOUN
fcis-31893	21	30	,	,	PUNCT
fcis-31893	21	31	including	include	VERB
fcis-31893	21	32	time	time	NOUN
fcis-31893	21	33	stretching	stretching	NOUN
fcis-31893	21	34	,	,	PUNCT
fcis-31893	21	35	gain	gain	VERB
fcis-31893	21	36	adjustment	adjustment	NOUN
fcis-31893	21	37	,	,	PUNCT
fcis-31893	21	38	and	and	CCONJ
fcis-31893	21	39	noise	noise	NOUN
fcis-31893	21	40	addition	addition	NOUN
fcis-31893	21	41	,	,	PUNCT
fcis-31893	21	42	which	which	PRON
fcis-31893	21	43	are	be	AUX
fcis-31893	21	44	specifically	specifically	ADV
fcis-31893	21	45	defined	define	VERB
fcis-31893	21	46	as	as	SCONJ
fcis-31893	21	47	follows	follow	VERB
fcis-31893	21	48	:	:	PUNCT
fcis-31893	21	49	(	(	PUNCT
fcis-31893	21	50	1	1	X
fcis-31893	21	51	)	)	PUNCT
fcis-31893	21	52	the	the	DET
fcis-31893	21	53	time	time	NOUN
fcis-31893	21	54	stretching	stretch	VERB
fcis-31893	21	55	technique	technique	NOUN
fcis-31893	21	56	extends	extend	VERB
fcis-31893	21	57	the	the	DET
fcis-31893	21	58	data	datum	NOUN
fcis-31893	21	59	by	by	ADP
fcis-31893	21	60	changing	change	VERB
fcis-31893	21	61	the	the	DET
fcis-31893	21	62	playback	playback	NOUN
fcis-31893	21	63	speed	speed	NOUN
fcis-31893	21	64	of	of	ADP
fcis-31893	21	65	the	the	DET
fcis-31893	21	66	audio	audio	ADJ
fcis-31893	21	67	signal	signal	NOUN
fcis-31893	21	68	,	,	PUNCT
fcis-31893	21	69	and	and	CCONJ
fcis-31893	21	70	its	its	PRON
fcis-31893	21	71	formula	formula	NOUN
fcis-31893	21	72	is	be	AUX
fcis-31893	21	73	defined	define	VERB
fcis-31893	21	74	as	as	SCONJ
fcis-31893	21	75	shown	show	VERB
fcis-31893	21	76	in	in	ADP
fcis-31893	21	77	(	(	PUNCT
fcis-31893	21	78	1	1	NUM
fcis-31893	21	79	)	)	PUNCT
fcis-31893	21	80	:	:	PUNCT
fcis-31893	22	1	'	'	PUNCT
fcis-31893	22	2	(	(	PUNCT
fcis-31893	22	3	t	t	NOUN
fcis-31893	22	4	)	)	PUNCT
fcis-31893	22	5	(	(	PUNCT
fcis-31893	22	6	)	)	PUNCT
fcis-31893	22	7	,	,	PUNCT
fcis-31893	22	8	{	{	PUNCT
fcis-31893	22	9	0.9,1.0,1.1	0.9,1.0,1.1	NOUN
fcis-31893	22	10	}	}	PUNCT
fcis-31893	22	11	t	t	NOUN
fcis-31893	22	12	x	x	SYM
fcis-31893	22	13	x	x	PROPN
fcis-31893	22	14			PROPN
fcis-31893	22	15			ADJ
fcis-31893	22	16	(	(	PUNCT
fcis-31893	22	17	1	1	NUM
fcis-31893	22	18	)	)	PUNCT
fcis-31893	22	19	in	in	ADP
fcis-31893	22	20	the	the	DET
fcis-31893	22	21	formula	formula	NOUN
fcis-31893	22	22	:	:	PUNCT
fcis-31893	22	23	x(t	x(t	PROPN
fcis-31893	22	24	)	)	PUNCT
fcis-31893	22	25	represents	represent	VERB
fcis-31893	22	26	the	the	DET
fcis-31893	22	27	original	original	ADJ
fcis-31893	22	28	audio	audio	NOUN
fcis-31893	22	29	signal	signal	NOUN
fcis-31893	22	30	;	;	PUNCT
fcis-31893	22	31	the	the	DET
fcis-31893	22	32	time	time	NOUN
fcis-31893	22	33	scaling	scale	VERB
fcis-31893	22	34	factor	factor	NOUN
fcis-31893	22	35	indicates	indicate	VERB
fcis-31893	22	36	that	that	SCONJ
fcis-31893	22	37	>	>	SYM
fcis-31893	22	38	1	1	NUM
fcis-31893	22	39	means	mean	VERB
fcis-31893	22	40	audio	audio	ADJ
fcis-31893	22	41	acceleration	acceleration	NOUN
fcis-31893	22	42	,	,	PUNCT
fcis-31893	22	43	and	and	CCONJ
fcis-31893	22	44	<	<	PRON
fcis-31893	22	45	1	1	NUM
fcis-31893	22	46	means	mean	VERB
fcis-31893	22	47	audio	audio	ADJ
fcis-31893	22	48	deceleration	deceleration	NOUN
fcis-31893	22	49	.	.	PUNCT
fcis-31893	23	1	gain	gain	NOUN
fcis-31893	23	2	adjustment	adjustment	NOUN
fcis-31893	23	3	achieves	achieve	VERB
fcis-31893	23	4	volume	volume	NOUN
fcis-31893	23	5	change	change	NOUN
fcis-31893	23	6	by	by	ADP
fcis-31893	23	7	scaling	scale	VERB
fcis-31893	23	8	the	the	DET
fcis-31893	23	9	amplitude	amplitude	NOUN
fcis-31893	23	10	of	of	ADP
fcis-31893	23	11	the	the	DET
fcis-31893	23	12	signal	signal	NOUN
fcis-31893	23	13	,	,	PUNCT
fcis-31893	23	14	as	as	SCONJ
fcis-31893	23	15	defined	define	VERB
fcis-31893	23	16	by	by	ADP
fcis-31893	23	17	the	the	DET
fcis-31893	23	18	formula	formula	NOUN
fcis-31893	23	19	shown	show	VERB
fcis-31893	23	20	in	in	ADP
fcis-31893	23	21	(	(	PUNCT
fcis-31893	23	22	2	2	NUM
fcis-31893	23	23	)	)	PUNCT
fcis-31893	23	24	:	:	PUNCT
fcis-31893	23	25	(	(	PUNCT
fcis-31893	23	26	)	)	PUNCT
fcis-31893	23	27	20	20	NUM
fcis-31893	23	28	'	'	NUM
fcis-31893	23	29	10	10	NUM
fcis-31893	23	30	gain	gain	NOUN
fcis-31893	23	31	db	db	ADP
fcis-31893	23	32	x	x	X
fcis-31893	23	33	x	x	X
fcis-31893	23	34			NUM
fcis-31893	23	35	(	(	PUNCT
fcis-31893	23	36	2	2	NUM
fcis-31893	23	37	)	)	PUNCT
fcis-31893	23	38	in	in	ADP
fcis-31893	23	39	the	the	DET
fcis-31893	23	40	formula	formula	NOUN
fcis-31893	23	41	:	:	PUNCT
fcis-31893	23	42	gain(db	gain(db	NOUN
fcis-31893	23	43	)	)	PUNCT
fcis-31893	23	44	refers	refer	VERB
fcis-31893	23	45	to	to	ADP
fcis-31893	23	46	the	the	DET
fcis-31893	23	47	magnitude	magnitude	NOUN
fcis-31893	23	48	of	of	ADP
fcis-31893	23	49	gain	gain	NOUN
fcis-31893	23	50	,	,	PUNCT
fcis-31893	23	51	typically	typically	ADV
fcis-31893	23	52	within	within	ADP
fcis-31893	23	53	the	the	DET
fcis-31893	23	54	range	range	NOUN
fcis-31893	23	55	of	of	ADP
fcis-31893	23	56	(	(	PUNCT
fcis-31893	23	57	-15	-15	PROPN
fcis-31893	23	58	,	,	PUNCT
fcis-31893	23	59	15	15	NUM
fcis-31893	23	60	)	)	PUNCT
fcis-31893	23	61	db	db	PROPN
fcis-31893	23	62	,	,	PUNCT
fcis-31893	23	63	used	use	VERB
fcis-31893	23	64	to	to	PART
fcis-31893	23	65	simulate	simulate	VERB
fcis-31893	23	66	different	different	ADJ
fcis-31893	23	67	recording	recording	NOUN
fcis-31893	23	68	environments	environment	NOUN
fcis-31893	23	69	.	.	PUNCT
fcis-31893	24	1	noise	noise	NOUN
fcis-31893	24	2	addition	addition	NOUN
fcis-31893	24	3	enhances	enhance	VERB
fcis-31893	24	4	the	the	DET
fcis-31893	24	5	audio	audio	NOUN
fcis-31893	24	6	signal	signal	NOUN
fcis-31893	24	7	by	by	ADP
fcis-31893	24	8	superimposing	superimpose	VERB
fcis-31893	24	9	background	background	NOUN
fcis-31893	24	10	noise	noise	NOUN
fcis-31893	24	11	,	,	PUNCT
fcis-31893	24	12	as	as	SCONJ
fcis-31893	24	13	defined	define	VERB
fcis-31893	24	14	by	by	ADP
fcis-31893	24	15	the	the	DET
fcis-31893	24	16	formula	formula	NOUN
fcis-31893	24	17	shown	show	VERB
fcis-31893	24	18	in	in	ADP
fcis-31893	24	19	(	(	PUNCT
fcis-31893	24	20	3	3	NUM
fcis-31893	24	21	):	):	SYM
fcis-31893	24	22	2	2	NUM
fcis-31893	24	23	10	10	NUM
fcis-31893	24	24	2	2	NUM
fcis-31893	24	25	||	||	NOUN
fcis-31893	24	26	||	||	NOUN
fcis-31893	24	27	'	'	PUNCT
fcis-31893	24	28	,	,	PUNCT
fcis-31893	24	29	snr=10	snr=10	PROPN
fcis-31893	24	30	log	log	VERB
fcis-31893	24	31	(	(	PUNCT
fcis-31893	24	32	)	)	PUNCT
fcis-31893	24	33	||	||	PUNCT
fcis-31893	25	1	||	||	NOUN
fcis-31893	26	1	x	x	PUNCT
fcis-31893	26	2	x	x	PUNCT
fcis-31893	26	3	x	x	SYM
fcis-31893	26	4	n	n	CCONJ
fcis-31893	26	5	n	n	PROPN
fcis-31893	26	6			PROPN
fcis-31893	26	7			ADJ
fcis-31893	26	8			PROPN
fcis-31893	26	9	(	(	PUNCT
fcis-31893	26	10	3	3	NUM
fcis-31893	26	11	)	)	PUNCT
fcis-31893	26	12	in	in	ADP
fcis-31893	26	13	the	the	DET
fcis-31893	26	14	equation	equation	NOUN
fcis-31893	26	15	:	:	PUNCT
fcis-31893	26	16	n	n	CCONJ
fcis-31893	26	17	represents	represent	VERB
fcis-31893	26	18	the	the	DET
fcis-31893	26	19	background	background	NOUN
fcis-31893	26	20	noise	noise	NOUN
fcis-31893	26	21	signal	signal	NOUN
fcis-31893	26	22	,	,	PUNCT
fcis-31893	26	23	snr	snr	PROPN
fcis-31893	26	24	is	be	AUX
fcis-31893	26	25	the	the	DET
fcis-31893	26	26	signal	signal	NOUN
fcis-31893	26	27	-	-	PUNCT
fcis-31893	26	28	to	to	ADP
fcis-31893	26	29	-	-	PUNCT
fcis-31893	26	30	noise	noise	NOUN
fcis-31893	26	31	ratio	ratio	NOUN
fcis-31893	26	32	,	,	PUNCT
fcis-31893	26	33	which	which	PRON
fcis-31893	26	34	controls	control	VERB
fcis-31893	26	35	the	the	DET
fcis-31893	26	36	ratio	ratio	NOUN
fcis-31893	26	37	of	of	ADP
fcis-31893	26	38	signal	signal	NOUN
fcis-31893	26	39	to	to	PART
fcis-31893	26	40	noise	noise	NOUN
fcis-31893	26	41	,	,	PUNCT
fcis-31893	26	42	typically	typically	ADV
fcis-31893	26	43	ranging	range	VERB
fcis-31893	26	44	from	from	ADP
fcis-31893	26	45	(	(	PUNCT
fcis-31893	26	46	10	10	NUM
fcis-31893	26	47	,	,	PUNCT
fcis-31893	26	48	50	50	NUM
fcis-31893	26	49	)	)	PUNCT
fcis-31893	26	50	db	db	PROPN
fcis-31893	26	51	.	.	PUNCT
fcis-31893	27	1	the	the	DET
fcis-31893	27	2	audio	audio	ADJ
fcis-31893	27	3	data	datum	NOUN
fcis-31893	27	4	has	have	AUX
fcis-31893	27	5	been	be	AUX
fcis-31893	27	6	extended	extend	VERB
fcis-31893	27	7	in	in	ADP
fcis-31893	27	8	the	the	DET
fcis-31893	27	9	time	time	NOUN
fcis-31893	27	10	domain	domain	NOUN
fcis-31893	27	11	and	and	CCONJ
fcis-31893	27	12	frequency	frequency	NOUN
fcis-31893	27	13	domain	domain	NOUN
fcis-31893	27	14	through	through	ADP
fcis-31893	27	15	the	the	DET
fcis-31893	27	16	above	above	ADJ
fcis-31893	27	17	enhancement	enhancement	NOUN
fcis-31893	27	18	methods	method	NOUN
fcis-31893	27	19	,	,	PUNCT
fcis-31893	27	20	40	40	NUM
fcis-31893	27	21	effectively	effectively	ADV
fcis-31893	27	22	improving	improve	VERB
fcis-31893	27	23	the	the	DET
fcis-31893	27	24	training	training	NOUN
fcis-31893	27	25	efficiency	efficiency	NOUN
fcis-31893	27	26	and	and	CCONJ
fcis-31893	27	27	robustness	robustness	NOUN
fcis-31893	27	28	of	of	ADP
fcis-31893	27	29	the	the	DET
fcis-31893	27	30	model	model	NOUN
fcis-31893	27	31	.	.	PUNCT
fcis-31893	28	1	1.4	1.4	NUM
fcis-31893	28	2	.	.	PUNCT
fcis-31893	28	3	audio	audio	ADJ
fcis-31893	28	4	feature	feature	NOUN
fcis-31893	28	5	extraction	extraction	NOUN
fcis-31893	28	6	early	early	ADJ
fcis-31893	28	7	feature	feature	NOUN
fcis-31893	28	8	fusion	fusion	NOUN
fcis-31893	28	9	refers	refer	VERB
fcis-31893	28	10	to	to	ADP
fcis-31893	28	11	the	the	DET
fcis-31893	28	12	combination	combination	NOUN
fcis-31893	28	13	of	of	ADP
fcis-31893	28	14	features	feature	NOUN
fcis-31893	28	15	from	from	ADP
fcis-31893	28	16	different	different	ADJ
fcis-31893	28	17	extraction	extraction	NOUN
fcis-31893	28	18	methods	method	NOUN
fcis-31893	28	19	at	at	ADP
fcis-31893	28	20	the	the	DET
fcis-31893	28	21	initial	initial	ADJ
fcis-31893	28	22	stage	stage	NOUN
fcis-31893	28	23	of	of	ADP
fcis-31893	28	24	audio	audio	ADJ
fcis-31893	28	25	signal	signal	NOUN
fcis-31893	28	26	processing	processing	NOUN
fcis-31893	28	27	,	,	PUNCT
fcis-31893	28	28	thereby	thereby	ADV
fcis-31893	28	29	providing	provide	VERB
fcis-31893	28	30	a	a	DET
fcis-31893	28	31	richer	rich	ADJ
fcis-31893	28	32	audio	audio	ADJ
fcis-31893	28	33	representation	representation	NOUN
fcis-31893	28	34	.	.	PUNCT
fcis-31893	29	1	in	in	ADP
fcis-31893	29	2	this	this	DET
fcis-31893	29	3	study	study	NOUN
fcis-31893	29	4	,	,	PUNCT
fcis-31893	29	5	we	we	PRON
fcis-31893	29	6	adopt	adopt	VERB
fcis-31893	29	7	an	an	DET
fcis-31893	29	8	early	early	ADJ
fcis-31893	29	9	feature	feature	NOUN
fcis-31893	29	10	fusion	fusion	NOUN
fcis-31893	29	11	method	method	NOUN
fcis-31893	29	12	using	use	VERB
fcis-31893	29	13	spectrogram	spectrogram	NOUN
fcis-31893	29	14	and	and	CCONJ
fcis-31893	29	15	mfcc	mfcc	NOUN
fcis-31893	29	16	,	,	PUNCT
fcis-31893	29	17	aiming	aim	VERB
fcis-31893	29	18	to	to	PART
fcis-31893	29	19	leverage	leverage	VERB
fcis-31893	29	20	the	the	DET
fcis-31893	29	21	advantages	advantage	NOUN
fcis-31893	29	22	of	of	ADP
fcis-31893	29	23	both	both	DET
fcis-31893	29	24	features	feature	NOUN
fcis-31893	29	25	to	to	PART
fcis-31893	29	26	enable	enable	VERB
fcis-31893	29	27	the	the	DET
fcis-31893	29	28	model	model	NOUN
fcis-31893	29	29	to	to	PART
fcis-31893	29	30	simultaneously	simultaneously	ADV
fcis-31893	29	31	capture	capture	VERB
fcis-31893	29	32	temporal	temporal	ADJ
fcis-31893	29	33	-	-	PUNCT
fcis-31893	29	34	frequency	frequency	NOUN
fcis-31893	29	35	information	information	NOUN
fcis-31893	29	36	and	and	CCONJ
fcis-31893	29	37	speech	speech	NOUN
fcis-31893	29	38	feature	feature	NOUN
fcis-31893	29	39	information	information	NOUN
fcis-31893	29	40	.	.	PUNCT
fcis-31893	30	1	below	below	ADV
fcis-31893	30	2	are	be	AUX
fcis-31893	30	3	the	the	DET
fcis-31893	30	4	relevant	relevant	ADJ
fcis-31893	30	5	formulas	formula	NOUN
fcis-31893	30	6	.	.	PUNCT
fcis-31893	31	1	the	the	DET
fcis-31893	31	2	spectrogram	spectrogram	NOUN
fcis-31893	31	3	feature	feature	NOUN
fcis-31893	31	4	extraction	extraction	NOUN
fcis-31893	31	5	method	method	NOUN
fcis-31893	31	6	calculates	calculate	VERB
fcis-31893	31	7	the	the	DET
fcis-31893	31	8	time	time	NOUN
fcis-31893	31	9	-	-	PUNCT
fcis-31893	31	10	frequency	frequency	NOUN
fcis-31893	31	11	distribution	distribution	NOUN
fcis-31893	31	12	of	of	ADP
fcis-31893	31	13	audio	audio	ADJ
fcis-31893	31	14	signals	signal	NOUN
fcis-31893	31	15	through	through	ADP
fcis-31893	31	16	the	the	DET
fcis-31893	31	17	short	short	ADJ
fcis-31893	31	18	-	-	PUNCT
fcis-31893	31	19	time	time	NOUN
fcis-31893	31	20	fourier	fourier	NOUN
fcis-31893	31	21	transform	transform	NOUN
fcis-31893	31	22	(	(	PUNCT
fcis-31893	31	23	stft	stft	PROPN
fcis-31893	31	24	)	)	PUNCT
fcis-31893	31	25	,	,	PUNCT
fcis-31893	31	26	with	with	ADP
fcis-31893	31	27	its	its	PRON
fcis-31893	31	28	formula	formula	NOUN
fcis-31893	31	29	defined	define	VERB
fcis-31893	31	30	as	as	SCONJ
fcis-31893	31	31	shown	show	VERB
fcis-31893	31	32	in	in	ADP
fcis-31893	31	33	(	(	PUNCT
fcis-31893	31	34	4	4	NUM
fcis-31893	31	35	):	):	SYM
fcis-31893	31	36	1	1	NUM
fcis-31893	31	37	2	2	NUM
fcis-31893	31	38	2	2	NUM
fcis-31893	31	39	0	0	NUM
fcis-31893	31	40	(	(	PUNCT
fcis-31893	31	41	,	,	PUNCT
fcis-31893	31	42	)	)	PUNCT
fcis-31893	32	1	|	|	ADV
fcis-31893	32	2	[	[	PUNCT
fcis-31893	32	3	]	]	X
fcis-31893	32	4	[	[	PUNCT
fcis-31893	32	5	]	]	X
fcis-31893	32	6	|	|	ADV
fcis-31893	32	7	n	n	PRON
fcis-31893	32	8	j	j	PROPN
fcis-31893	32	9	fn	fn	VERB
fcis-31893	32	10	n	n	PROPN
fcis-31893	32	11	s	s	PROPN
fcis-31893	32	12	t	t	NOUN
fcis-31893	32	13	f	f	X
fcis-31893	32	14	x	x	PROPN
fcis-31893	32	15	n	n	PROPN
fcis-31893	32	16	w	w	PROPN
fcis-31893	32	17	n	n	ADP
fcis-31893	32	18	t	t	X
fcis-31893	32	19	e	e	X
fcis-31893	32	20			PROPN
fcis-31893	32	21			PROPN
fcis-31893	32	22			PROPN
fcis-31893	32	23			PROPN
fcis-31893	33	1			ADJ
fcis-31893	33	2			ADJ
fcis-31893	33	3			NOUN
fcis-31893	33	4			X
fcis-31893	33	5	(	(	PUNCT
fcis-31893	33	6	4	4	NUM
fcis-31893	33	7	)	)	PUNCT
fcis-31893	33	8	in	in	ADP
fcis-31893	33	9	the	the	DET
fcis-31893	33	10	formula	formula	NOUN
fcis-31893	33	11	:	:	PUNCT
fcis-31893	33	12	x[n	x[n	PROPN
fcis-31893	33	13	]	]	PUNCT
fcis-31893	33	14	represents	represent	VERB
fcis-31893	33	15	the	the	DET
fcis-31893	33	16	input	input	NOUN
fcis-31893	33	17	audio	audio	NOUN
fcis-31893	33	18	signal	signal	NOUN
fcis-31893	33	19	;	;	PUNCT
fcis-31893	33	20	w[n	w[n	PROPN
fcis-31893	33	21	]	]	PUNCT
fcis-31893	33	22	represents	represent	VERB
fcis-31893	33	23	the	the	DET
fcis-31893	33	24	window	window	NOUN
fcis-31893	33	25	function	function	NOUN
fcis-31893	33	26	;	;	PUNCT
fcis-31893	33	27	t	t	PROPN
fcis-31893	33	28	and	and	CCONJ
fcis-31893	33	29	f	f	PROPN
fcis-31893	33	30	represent	represent	VERB
fcis-31893	33	31	the	the	DET
fcis-31893	33	32	time	time	NOUN
fcis-31893	33	33	and	and	CCONJ
fcis-31893	33	34	frequency	frequency	NOUN
fcis-31893	33	35	indices	index	NOUN
fcis-31893	33	36	,	,	PUNCT
fcis-31893	33	37	respectively	respectively	ADV
fcis-31893	33	38	.	.	PUNCT
fcis-31893	34	1	the	the	DET
fcis-31893	34	2	mfcc	mfcc	NOUN
fcis-31893	34	3	feature	feature	NOUN
fcis-31893	34	4	extraction	extraction	NOUN
fcis-31893	34	5	method	method	NOUN
fcis-31893	34	6	is	be	AUX
fcis-31893	34	7	based	base	VERB
fcis-31893	34	8	on	on	ADP
fcis-31893	34	9	the	the	DET
fcis-31893	34	10	calculation	calculation	NOUN
fcis-31893	34	11	of	of	ADP
fcis-31893	34	12	mel	mel	PROPN
fcis-31893	34	13	frequency	frequency	PROPN
fcis-31893	34	14	cepstral	cepstral	ADJ
fcis-31893	34	15	coefficients	coefficient	NOUN
fcis-31893	34	16	using	use	VERB
fcis-31893	34	17	a	a	DET
fcis-31893	34	18	mel	mel	PROPN
fcis-31893	34	19	filter	filter	PROPN
fcis-31893	34	20	bank	bank	PROPN
fcis-31893	34	21	,	,	PUNCT
fcis-31893	34	22	with	with	ADP
fcis-31893	34	23	the	the	DET
fcis-31893	34	24	formula	formula	NOUN
fcis-31893	34	25	defined	define	VERB
fcis-31893	34	26	as	as	SCONJ
fcis-31893	34	27	shown	show	VERB
fcis-31893	34	28	in	in	ADP
fcis-31893	34	29	(	(	PUNCT
fcis-31893	34	30	5	5	NUM
fcis-31893	34	31	):	):	SYM
fcis-31893	34	32	1	1	NUM
fcis-31893	34	33	(	(	PUNCT
fcis-31893	34	34	0.5	0.5	NUM
fcis-31893	34	35	)	)	PUNCT
fcis-31893	34	36	(	(	PUNCT
fcis-31893	34	37	m	m	NOUN
fcis-31893	34	38	)	)	PUNCT
fcis-31893	34	39	log	log	NOUN
fcis-31893	34	40	(	(	PUNCT
fcis-31893	34	41	(	(	PUNCT
fcis-31893	34	42	)	)	PUNCT
fcis-31893	34	43	)	)	PUNCT
fcis-31893	34	44	cos	cos	PROPN
fcis-31893	34	45	(	(	PUNCT
fcis-31893	34	46	)	)	PUNCT
fcis-31893	34	47	k	k	PROPN
fcis-31893	35	1	k	k	PROPN
fcis-31893	35	2	m	m	VERB
fcis-31893	35	3	k	k	PROPN
fcis-31893	35	4	mfcc	mfcc	NOUN
fcis-31893	35	5	s	s	PART
fcis-31893	35	6	k	k	PROPN
fcis-31893	35	7	k	k	PROPN
fcis-31893	35	8			PROPN
fcis-31893	35	9			PROPN
fcis-31893	35	10			PROPN
fcis-31893	35	11			PROPN
fcis-31893	35	12			X
fcis-31893	35	13	(	(	PUNCT
fcis-31893	35	14	5	5	NUM
fcis-31893	35	15	)	)	PUNCT
fcis-31893	35	16	in	in	ADP
fcis-31893	35	17	the	the	DET
fcis-31893	35	18	formula	formula	NOUN
fcis-31893	35	19	:	:	PUNCT
fcis-31893	35	20	k	k	X
fcis-31893	35	21	represents	represent	VERB
fcis-31893	35	22	the	the	DET
fcis-31893	35	23	number	number	NOUN
fcis-31893	35	24	of	of	ADP
fcis-31893	35	25	mel	mel	PROPN
fcis-31893	35	26	filters	filter	NOUN
fcis-31893	35	27	,	,	PUNCT
fcis-31893	35	28	s(k	s(k	ADV
fcis-31893	35	29	)	)	PUNCT
fcis-31893	35	30	represents	represent	VERB
fcis-31893	35	31	the	the	DET
fcis-31893	35	32	result	result	NOUN
fcis-31893	35	33	of	of	ADP
fcis-31893	35	34	the	the	DET
fcis-31893	35	35	pinpu	pinpu	ADJ
fcis-31893	35	36	image	image	NOUN
fcis-31893	35	37	after	after	ADP
fcis-31893	35	38	passing	pass	VERB
fcis-31893	35	39	through	through	ADP
fcis-31893	35	40	the	the	DET
fcis-31893	35	41	mel	mel	PROPN
fcis-31893	35	42	filter	filter	PROPN
fcis-31893	35	43	bank	bank	PROPN
fcis-31893	35	44	.	.	PUNCT
fcis-31893	36	1	the	the	DET
fcis-31893	36	2	features	feature	NOUN
fcis-31893	36	3	of	of	ADP
fcis-31893	36	4	the	the	DET
fcis-31893	36	5	spectrogram	spectrogram	NOUN
fcis-31893	36	6	and	and	CCONJ
fcis-31893	36	7	mfcc	mfcc	NOUN
fcis-31893	36	8	are	be	AUX
fcis-31893	36	9	dimensionally	dimensionally	ADV
fcis-31893	36	10	concatenated	concatenate	VERB
fcis-31893	36	11	to	to	PART
fcis-31893	36	12	form	form	VERB
fcis-31893	36	13	a	a	DET
fcis-31893	36	14	comprehensive	comprehensive	ADJ
fcis-31893	36	15	feature	feature	NOUN
fcis-31893	36	16	representation	representation	NOUN
fcis-31893	36	17	,	,	PUNCT
fcis-31893	36	18	as	as	SCONJ
fcis-31893	36	19	defined	define	VERB
fcis-31893	36	20	by	by	ADP
fcis-31893	36	21	the	the	DET
fcis-31893	36	22	formula	formula	NOUN
fcis-31893	36	23	shown	show	VERB
fcis-31893	36	24	in	in	ADP
fcis-31893	36	25	(	(	PUNCT
fcis-31893	36	26	6	6	NUM
fcis-31893	36	27	):	):	PUNCT
fcis-31893	36	28	fused	fuse	VERB
fcis-31893	36	29	(	(	PUNCT
fcis-31893	36	30	,	,	PUNCT
fcis-31893	36	31	)	)	PUNCT
fcis-31893	36	32	spectrogram	spectrogram	NOUN
fcis-31893	36	33	mfccf	mfccf	NOUN
fcis-31893	36	34	concat	concat	NOUN
fcis-31893	36	35	f	f	PROPN
fcis-31893	36	36	f	f	PROPN
fcis-31893	36	37	(	(	PUNCT
fcis-31893	36	38	6	6	NUM
fcis-31893	36	39	)	)	PUNCT
fcis-31893	36	40	in	in	ADP
fcis-31893	36	41	the	the	DET
fcis-31893	36	42	formula	formula	NOUN
fcis-31893	36	43	:	:	PUNCT
fcis-31893	36	44	indicates	indicate	VERB
fcis-31893	36	45	the	the	DET
fcis-31893	36	46	result	result	NOUN
fcis-31893	36	47	of	of	ADP
fcis-31893	36	48	the	the	DET
fcis-31893	36	49	dimension	dimension	NOUN
fcis-31893	36	50	concatenation	concatenation	NOUN
fcis-31893	36	51	of	of	ADP
fcis-31893	36	52	the	the	DET
fcis-31893	36	53	features	feature	NOUN
fcis-31893	36	54	of	of	ADP
fcis-31893	36	55	spectrogram	spectrogram	NOUN
fcis-31893	36	56	and	and	CCONJ
fcis-31893	36	57	mfcc	mfcc	NOUN
fcis-31893	36	58	.	.	PUNCT
fcis-31893	37	1	the	the	DET
fcis-31893	37	2	specific	specific	ADJ
fcis-31893	37	3	process	process	NOUN
fcis-31893	37	4	begins	begin	VERB
fcis-31893	37	5	by	by	ADP
fcis-31893	37	6	extracting	extract	VERB
fcis-31893	37	7	the	the	DET
fcis-31893	37	8	spectrogram	spectrogram	NOUN
fcis-31893	37	9	and	and	CCONJ
fcis-31893	37	10	mfcc	mfcc	NOUN
fcis-31893	37	11	from	from	ADP
fcis-31893	37	12	the	the	DET
fcis-31893	37	13	audio	audio	ADJ
fcis-31893	37	14	signal	signal	NOUN
fcis-31893	37	15	.	.	PUNCT
fcis-31893	38	1	figure	figure	NOUN
fcis-31893	38	2	1	1	NUM
fcis-31893	38	3	shows	show	VERB
fcis-31893	38	4	a	a	DET
fcis-31893	38	5	diagram	diagram	NOUN
fcis-31893	38	6	of	of	ADP
fcis-31893	38	7	the	the	DET
fcis-31893	38	8	spectrogram	spectrogram	NOUN
fcis-31893	38	9	extracted	extract	VERB
fcis-31893	38	10	alone	alone	ADV
fcis-31893	38	11	,	,	PUNCT
fcis-31893	38	12	while	while	SCONJ
fcis-31893	38	13	figure	figure	NOUN
fcis-31893	38	14	2	2	NUM
fcis-31893	38	15	shows	show	VERB
fcis-31893	38	16	a	a	DET
fcis-31893	38	17	diagram	diagram	NOUN
fcis-31893	38	18	of	of	ADP
fcis-31893	38	19	the	the	DET
fcis-31893	38	20	mfcc	mfcc	NOUN
fcis-31893	38	21	extracted	extract	VERB
fcis-31893	38	22	alone	alone	ADV
fcis-31893	38	23	.	.	PUNCT
fcis-31893	39	1	the	the	DET
fcis-31893	39	2	spectrogram	spectrogram	NOUN
fcis-31893	39	3	provides	provide	VERB
fcis-31893	39	4	the	the	DET
fcis-31893	39	5	frequency	frequency	NOUN
fcis-31893	39	6	and	and	CCONJ
fcis-31893	39	7	time	time	NOUN
fcis-31893	39	8	structure	structure	NOUN
fcis-31893	39	9	of	of	ADP
fcis-31893	39	10	the	the	DET
fcis-31893	39	11	audio	audio	ADJ
fcis-31893	39	12	signal	signal	NOUN
fcis-31893	39	13	,	,	PUNCT
fcis-31893	39	14	whereas	whereas	SCONJ
fcis-31893	39	15	the	the	DET
fcis-31893	39	16	mfcc	mfcc	NOUN
fcis-31893	39	17	focuses	focus	VERB
fcis-31893	39	18	on	on	ADP
fcis-31893	39	19	extracting	extract	VERB
fcis-31893	39	20	the	the	DET
fcis-31893	39	21	speech	speech	NOUN
fcis-31893	39	22	features	feature	NOUN
fcis-31893	39	23	of	of	ADP
fcis-31893	39	24	the	the	DET
fcis-31893	39	25	audio	audio	ADJ
fcis-31893	39	26	signal	signal	NOUN
fcis-31893	39	27	.	.	PUNCT
fcis-31893	40	1	after	after	ADP
fcis-31893	40	2	obtaining	obtain	VERB
fcis-31893	40	3	these	these	DET
fcis-31893	40	4	two	two	NUM
fcis-31893	40	5	features	feature	NOUN
fcis-31893	40	6	,	,	PUNCT
fcis-31893	40	7	we	we	PRON
fcis-31893	40	8	concatenate	concatenate	VERB
fcis-31893	40	9	them	they	PRON
fcis-31893	40	10	along	along	ADP
fcis-31893	40	11	the	the	DET
fcis-31893	40	12	feature	feature	NOUN
fcis-31893	40	13	dimension	dimension	NOUN
fcis-31893	40	14	as	as	SCONJ
fcis-31893	40	15	shown	show	VERB
fcis-31893	40	16	in	in	ADP
fcis-31893	40	17	figure	figure	NOUN
fcis-31893	40	18	3	3	NUM
fcis-31893	40	19	.	.	PUNCT
fcis-31893	41	1	this	this	DET
fcis-31893	41	2	way	way	NOUN
fcis-31893	41	3	,	,	PUNCT
fcis-31893	41	4	the	the	DET
fcis-31893	41	5	information	information	NOUN
fcis-31893	41	6	in	in	ADP
fcis-31893	41	7	the	the	DET
fcis-31893	41	8	spectrogram	spectrogram	NOUN
fcis-31893	41	9	and	and	CCONJ
fcis-31893	41	10	mfcc	mfcc	NOUN
fcis-31893	41	11	can	can	AUX
fcis-31893	41	12	be	be	AUX
fcis-31893	41	13	fully	fully	ADV
fcis-31893	41	14	integrated	integrate	VERB
fcis-31893	41	15	to	to	PART
fcis-31893	41	16	form	form	VERB
fcis-31893	41	17	a	a	DET
fcis-31893	41	18	comprehensive	comprehensive	ADJ
fcis-31893	41	19	feature	feature	NOUN
fcis-31893	41	20	representation	representation	NOUN
fcis-31893	41	21	.	.	PUNCT
fcis-31893	42	1	the	the	DET
fcis-31893	42	2	fused	fuse	VERB
fcis-31893	42	3	features	feature	NOUN
fcis-31893	42	4	undergo	undergo	VERB
fcis-31893	42	5	normalization	normalization	NOUN
fcis-31893	42	6	to	to	PART
fcis-31893	42	7	enhance	enhance	VERB
fcis-31893	42	8	the	the	DET
fcis-31893	42	9	stability	stability	NOUN
fcis-31893	42	10	during	during	ADP
fcis-31893	42	11	model	model	NOUN
fcis-31893	42	12	training	training	NOUN
fcis-31893	42	13	.	.	PUNCT
fcis-31893	43	1	by	by	ADP
fcis-31893	43	2	removing	remove	VERB
fcis-31893	43	3	the	the	DET
fcis-31893	43	4	mean	mean	NOUN
fcis-31893	43	5	of	of	ADP
fcis-31893	43	6	the	the	DET
fcis-31893	43	7	features	feature	NOUN
fcis-31893	43	8	and	and	CCONJ
fcis-31893	43	9	performing	perform	VERB
fcis-31893	43	10	standardization	standardization	NOUN
fcis-31893	43	11	,	,	PUNCT
fcis-31893	43	12	the	the	DET
fcis-31893	43	13	amplitude	amplitude	NOUN
fcis-31893	43	14	differences	difference	NOUN
fcis-31893	43	15	between	between	ADP
fcis-31893	43	16	different	different	ADJ
fcis-31893	43	17	audio	audio	NOUN
fcis-31893	43	18	signals	signal	NOUN
fcis-31893	43	19	can	can	AUX
fcis-31893	43	20	be	be	AUX
fcis-31893	43	21	reduced	reduce	VERB
fcis-31893	43	22	,	,	PUNCT
fcis-31893	43	23	thus	thus	ADV
fcis-31893	43	24	helping	help	VERB
fcis-31893	43	25	to	to	PART
fcis-31893	43	26	improve	improve	VERB
fcis-31893	43	27	the	the	DET
fcis-31893	43	28	learning	learning	NOUN
fcis-31893	43	29	efficiency	efficiency	NOUN
fcis-31893	43	30	of	of	ADP
fcis-31893	43	31	the	the	DET
fcis-31893	43	32	model	model	NOUN
fcis-31893	43	33	.	.	PUNCT
fcis-31893	44	1	the	the	DET
fcis-31893	44	2	result	result	NOUN
fcis-31893	44	3	of	of	ADP
fcis-31893	44	4	feature	feature	NOUN
fcis-31893	44	5	fusion	fusion	NOUN
fcis-31893	44	6	is	be	AUX
fcis-31893	44	7	a	a	DET
fcis-31893	44	8	feature	feature	NOUN
fcis-31893	44	9	map	map	NOUN
fcis-31893	44	10	that	that	PRON
fcis-31893	44	11	contains	contain	VERB
fcis-31893	44	12	composite	composite	ADJ
fcis-31893	44	13	information	information	NOUN
fcis-31893	44	14	from	from	ADP
fcis-31893	44	15	both	both	CCONJ
fcis-31893	44	16	the	the	DET
fcis-31893	44	17	spectrogram	spectrogram	NOUN
fcis-31893	44	18	and	and	CCONJ
fcis-31893	44	19	mfcc	mfcc	NOUN
fcis-31893	44	20	.	.	PUNCT
fcis-31893	45	1	in	in	ADP
fcis-31893	45	2	the	the	DET
fcis-31893	45	3	time	time	NOUN
fcis-31893	45	4	-	-	PUNCT
fcis-31893	45	5	frequency	frequency	NOUN
fcis-31893	45	6	domain	domain	NOUN
fcis-31893	45	7	,	,	PUNCT
fcis-31893	45	8	this	this	DET
fcis-31893	45	9	fusion	fusion	NOUN
fcis-31893	45	10	can	can	AUX
fcis-31893	45	11	retain	retain	VERB
fcis-31893	45	12	the	the	DET
fcis-31893	45	13	broad	broad	ADJ
fcis-31893	45	14	coverage	coverage	NOUN
fcis-31893	45	15	of	of	ADP
fcis-31893	45	16	the	the	DET
fcis-31893	45	17	spectrogram	spectrogram	NOUN
fcis-31893	45	18	in	in	ADP
fcis-31893	45	19	the	the	DET
fcis-31893	45	20	frequency	frequency	NOUN
fcis-31893	45	21	domain	domain	NOUN
fcis-31893	45	22	as	as	ADV
fcis-31893	45	23	well	well	ADV
fcis-31893	45	24	as	as	ADP
fcis-31893	45	25	the	the	DET
fcis-31893	45	26	detailed	detailed	ADJ
fcis-31893	45	27	description	description	NOUN
fcis-31893	45	28	of	of	ADP
fcis-31893	45	29	speech	speech	NOUN
fcis-31893	45	30	feature	feature	NOUN
fcis-31893	45	31	extraction	extraction	NOUN
fcis-31893	45	32	provided	provide	VERB
fcis-31893	45	33	by	by	ADP
fcis-31893	45	34	the	the	DET
fcis-31893	45	35	mfcc	mfcc	NOUN
fcis-31893	45	36	.	.	PUNCT
fcis-31893	46	1	this	this	PRON
fcis-31893	46	2	enables	enable	VERB
fcis-31893	46	3	the	the	DET
fcis-31893	46	4	model	model	NOUN
fcis-31893	46	5	to	to	PART
fcis-31893	46	6	understand	understand	VERB
fcis-31893	46	7	the	the	DET
fcis-31893	46	8	information	information	NOUN
fcis-31893	46	9	in	in	ADP
fcis-31893	46	10	the	the	DET
fcis-31893	46	11	audio	audio	NOUN
fcis-31893	46	12	signal	signal	NOUN
fcis-31893	46	13	more	more	ADV
fcis-31893	46	14	comprehensively	comprehensively	ADV
fcis-31893	46	15	,	,	PUNCT
fcis-31893	46	16	making	make	VERB
fcis-31893	46	17	it	it	PRON
fcis-31893	46	18	particularly	particularly	ADV
fcis-31893	46	19	suitable	suitable	ADJ
fcis-31893	46	20	for	for	ADP
fcis-31893	46	21	tasks	task	NOUN
fcis-31893	46	22	such	such	ADJ
fcis-31893	46	23	as	as	ADP
fcis-31893	46	24	audio	audio	ADJ
fcis-31893	46	25	classification	classification	NOUN
fcis-31893	46	26	and	and	CCONJ
fcis-31893	46	27	speech	speech	NOUN
fcis-31893	46	28	recognition	recognition	NOUN
fcis-31893	46	29	.	.	PUNCT
fcis-31893	47	1	fig	fig	NOUN
fcis-31893	47	2	1	1	NUM
fcis-31893	47	3	.	.	PUNCT
fcis-31893	47	4	spectrogram	spectrogram	NOUN
fcis-31893	47	5	fig	fig	NOUN
fcis-31893	47	6	2	2	NUM
fcis-31893	47	7	.	.	NOUN
fcis-31893	47	8	mfcc	mfcc	NOUN
fcis-31893	47	9	fig	fig	NOUN
fcis-31893	47	10	3	3	NUM
fcis-31893	47	11	.	.	PUNCT
fcis-31893	47	12	feature	feature	NOUN
fcis-31893	47	13	fusion	fusion	NOUN
fcis-31893	47	14	2	2	NUM
fcis-31893	47	15	.	.	PUNCT
fcis-31893	47	16	model	model	NOUN
fcis-31893	47	17	design	design	PROPN
fcis-31893	47	18	convnext	convnext	PROPN
fcis-31893	47	19	is	be	AUX
fcis-31893	47	20	an	an	DET
fcis-31893	47	21	improved	improved	ADJ
fcis-31893	47	22	convolutional	convolutional	ADJ
fcis-31893	47	23	neural	neural	ADJ
fcis-31893	47	24	network	network	NOUN
fcis-31893	47	25	proposed	propose	VERB
fcis-31893	47	26	in	in	ADP
fcis-31893	47	27	recent	recent	ADJ
fcis-31893	47	28	years	year	NOUN
fcis-31893	47	29	,	,	PUNCT
fcis-31893	47	30	characterized	characterize	VERB
fcis-31893	47	31	by	by	ADP
fcis-31893	47	32	its	its	PRON
fcis-31893	47	33	efficient	efficient	ADJ
fcis-31893	47	34	feature	feature	NOUN
fcis-31893	47	35	extraction	extraction	NOUN
fcis-31893	47	36	capability	capability	NOUN
fcis-31893	47	37	and	and	CCONJ
fcis-31893	47	38	lower	low	ADJ
fcis-31893	47	39	computational	computational	ADJ
fcis-31893	47	40	overhead	overhead	NOUN
fcis-31893	47	41	.	.	PUNCT
fcis-31893	48	1	compared	compare	VERB
fcis-31893	48	2	to	to	ADP
fcis-31893	48	3	traditional	traditional	ADJ
fcis-31893	48	4	convolutional	convolutional	ADJ
fcis-31893	48	5	neural	neural	ADJ
fcis-31893	48	6	networks	network	NOUN
fcis-31893	48	7	(	(	PUNCT
fcis-31893	48	8	such	such	ADJ
fcis-31893	48	9	as	as	ADP
fcis-31893	48	10	resnet	resnet	NOUN
fcis-31893	48	11	[	[	X
fcis-31893	48	12	4	4	NUM
fcis-31893	48	13	]	]	NUM
fcis-31893	48	14	)	)	PUNCT
fcis-31893	48	15	,	,	PUNCT
fcis-31893	48	16	convnext	convnext	PROPN
fcis-31893	48	17	employs	employ	VERB
fcis-31893	48	18	deeper	deep	ADJ
fcis-31893	48	19	network	network	NOUN
fcis-31893	48	20	layers	layer	NOUN
fcis-31893	48	21	and	and	CCONJ
fcis-31893	48	22	optimized	optimize	VERB
fcis-31893	48	23	convolution	convolution	NOUN
fcis-31893	48	24	operations	operation	NOUN
fcis-31893	48	25	,	,	PUNCT
fcis-31893	48	26	effectively	effectively	ADV
fcis-31893	48	27	extracting	extract	VERB
fcis-31893	48	28	local	local	ADJ
fcis-31893	48	29	features	feature	NOUN
fcis-31893	48	30	from	from	ADP
fcis-31893	48	31	audio	audio	ADJ
fcis-31893	48	32	signals	signal	NOUN
fcis-31893	48	33	,	,	PUNCT
fcis-31893	48	34	and	and	CCONJ
fcis-31893	48	35	demonstrating	demonstrate	VERB
fcis-31893	48	36	excellent	excellent	ADJ
fcis-31893	48	37	performance	performance	NOUN
fcis-31893	48	38	particularly	particularly	ADV
fcis-31893	48	39	when	when	SCONJ
fcis-31893	48	40	processing	processing	NOUN
fcis-31893	48	41	time	time	NOUN
fcis-31893	48	42	-	-	PUNCT
fcis-31893	48	43	frequency	frequency	NOUN
fcis-31893	48	44	images	image	NOUN
fcis-31893	48	45	(	(	PUNCT
fcis-31893	48	46	such	such	ADJ
fcis-31893	48	47	as	as	ADP
fcis-31893	48	48	spectrograms	spectrogram	NOUN
fcis-31893	48	49	)	)	PUNCT
fcis-31893	48	50	.	.	PUNCT
fcis-31893	49	1	in	in	ADP
fcis-31893	49	2	this	this	DET
fcis-31893	49	3	study	study	NOUN
fcis-31893	49	4	,	,	PUNCT
fcis-31893	49	5	convnext	convnext	PROPN
fcis-31893	49	6	was	be	AUX
fcis-31893	49	7	used	use	VERB
fcis-31893	49	8	as	as	ADP
fcis-31893	49	9	the	the	DET
fcis-31893	49	10	base	base	NOUN
fcis-31893	49	11	model	model	NOUN
fcis-31893	49	12	for	for	ADP
fcis-31893	49	13	noise	noise	NOUN
fcis-31893	49	14	classification	classification	NOUN
fcis-31893	49	15	.	.	PUNCT
fcis-31893	50	1	the	the	DET
fcis-31893	50	2	design	design	NOUN
fcis-31893	50	3	of	of	ADP
fcis-31893	50	4	convnext	convnext	NOUN
fcis-31893	50	5	includes	include	VERB
fcis-31893	50	6	multiple	multiple	ADJ
fcis-31893	50	7	convolutional	convolutional	ADJ
fcis-31893	50	8	layers	layer	NOUN
fcis-31893	50	9	and	and	CCONJ
fcis-31893	50	10	residual	residual	ADJ
fcis-31893	50	11	connections	connection	NOUN
fcis-31893	50	12	,	,	PUNCT
fcis-31893	50	13	with	with	ADP
fcis-31893	50	14	each	each	DET
fcis-31893	50	15	layer	layer	NOUN
fcis-31893	50	16	's	's	PART
fcis-31893	50	17	convolution	convolution	NOUN
fcis-31893	50	18	kernel	kernel	NOUN
fcis-31893	50	19	size	size	NOUN
fcis-31893	50	20	,	,	PUNCT
fcis-31893	50	21	stride	stride	NOUN
fcis-31893	50	22	,	,	PUNCT
fcis-31893	50	23	and	and	CCONJ
fcis-31893	50	24	number	number	NOUN
fcis-31893	50	25	of	of	ADP
fcis-31893	50	26	channels	channel	NOUN
fcis-31893	50	27	carefully	carefully	ADV
fcis-31893	50	28	designed	design	VERB
fcis-31893	50	29	to	to	PART
fcis-31893	50	30	meet	meet	VERB
fcis-31893	50	31	the	the	DET
fcis-31893	50	32	extraction	extraction	NOUN
fcis-31893	50	33	needs	need	NOUN
fcis-31893	50	34	of	of	ADP
fcis-31893	50	35	different	different	ADJ
fcis-31893	50	36	types	type	NOUN
fcis-31893	50	37	of	of	ADP
fcis-31893	50	38	audio	audio	NOUN
fcis-31893	50	39	features	feature	NOUN
fcis-31893	50	40	.	.	PUNCT
fcis-31893	51	1	by	by	ADP
fcis-31893	51	2	performing	perform	VERB
fcis-31893	51	3	multi	multi	ADJ
fcis-31893	51	4	-	-	ADJ
fcis-31893	51	5	level	level	ADJ
fcis-31893	51	6	feature	feature	NOUN
fcis-31893	51	7	extraction	extraction	NOUN
fcis-31893	51	8	on	on	ADP
fcis-31893	51	9	audio	audio	ADJ
fcis-31893	51	10	signals	signal	NOUN
fcis-31893	51	11	,	,	PUNCT
fcis-31893	51	12	convnext	convnext	NOUN
fcis-31893	51	13	can	can	AUX
fcis-31893	51	14	effectively	effectively	ADV
fcis-31893	51	15	capture	capture	VERB
fcis-31893	51	16	complex	complex	ADJ
fcis-31893	51	17	frequency	frequency	NOUN
fcis-31893	51	18	and	and	CCONJ
fcis-31893	51	19	temporal	temporal	ADJ
fcis-31893	51	20	patterns	pattern	NOUN
fcis-31893	51	21	,	,	PUNCT
fcis-31893	51	22	enhancing	enhance	VERB
fcis-31893	51	23	the	the	DET
fcis-31893	51	24	model	model	NOUN
fcis-31893	51	25	's	's	PART
fcis-31893	51	26	ability	ability	NOUN
fcis-31893	51	27	to	to	PART
fcis-31893	51	28	classify	classify	VERB
fcis-31893	51	29	different	different	ADJ
fcis-31893	51	30	noise	noise	NOUN
fcis-31893	51	31	sources	source	NOUN
fcis-31893	51	32	.	.	PUNCT
fcis-31893	52	1	2.1	2.1	NUM
fcis-31893	52	2	.	.	PUNCT
fcis-31893	53	1	network	network	NOUN
fcis-31893	53	2	setup	setup	NOUN
fcis-31893	53	3	this	this	DET
fcis-31893	53	4	article	article	NOUN
fcis-31893	53	5	presents	present	VERB
fcis-31893	53	6	a	a	DET
fcis-31893	53	7	multi	multi	ADJ
fcis-31893	53	8	-	-	ADJ
fcis-31893	53	9	scale	scale	ADJ
fcis-31893	53	10	adaptive	adaptive	ADJ
fcis-31893	53	11	attention	attention	NOUN
fcis-31893	53	12	mechanism	mechanism	NOUN
fcis-31893	53	13	based	base	VERB
fcis-31893	53	14	on	on	ADP
fcis-31893	53	15	frequency	frequency	NOUN
fcis-31893	53	16	domain	domain	NOUN
fcis-31893	53	17	enhancement	enhancement	NOUN
fcis-31893	53	18	,	,	PUNCT
fcis-31893	53	19	aimed	aim	VERB
fcis-31893	53	20	at	at	ADP
fcis-31893	53	21	optimizing	optimize	VERB
fcis-31893	53	22	feature	feature	NOUN
fcis-31893	53	23	extraction	extraction	NOUN
fcis-31893	53	24	capabilities	capability	NOUN
fcis-31893	53	25	in	in	ADP
fcis-31893	53	26	audio	audio	ADJ
fcis-31893	53	27	analysis	analysis	NOUN
fcis-31893	53	28	tasks	task	NOUN
fcis-31893	53	29	.	.	PUNCT
fcis-31893	54	1	the	the	DET
fcis-31893	54	2	model	model	NOUN
fcis-31893	54	3	uses	use	VERB
fcis-31893	54	4	a	a	DET
fcis-31893	54	5	convnext	convnext	ADJ
fcis-31893	54	6	structure	structure	NOUN
fcis-31893	54	7	and	and	CCONJ
fcis-31893	54	8	incorporates	incorporate	VERB
fcis-31893	54	9	an	an	DET
fcis-31893	54	10	improved	improve	VERB
fcis-31893	54	11	enhanced	enhanced	ADJ
fcis-31893	54	12	frequency	frequency	NOUN
fcis-31893	54	13	channel	channel	NOUN
fcis-31893	54	14	attention	attention	NOUN
fcis-31893	54	15	mechanism	mechanism	NOUN
fcis-31893	54	16	,	,	PUNCT
fcis-31893	54	17	which	which	PRON
fcis-31893	54	18	enhances	enhance	VERB
fcis-31893	54	19	feature	feature	NOUN
fcis-31893	54	20	capture	capture	NOUN
fcis-31893	54	21	expression	expression	NOUN
fcis-31893	54	22	through	through	ADP
fcis-31893	54	23	multi	multi	ADJ
fcis-31893	54	24	-	-	ADJ
fcis-31893	54	25	scale	scale	ADJ
fcis-31893	54	26	convolution	convolution	NOUN
fcis-31893	54	27	and	and	CCONJ
fcis-31893	54	28	phase	phase	NOUN
fcis-31893	54	29	information	information	NOUN
fcis-31893	54	30	,	,	PUNCT
fcis-31893	54	31	as	as	SCONJ
fcis-31893	54	32	shown	show	VERB
fcis-31893	54	33	in	in	ADP
fcis-31893	54	34	figure	figure	NOUN
fcis-31893	54	35	4	4	NUM
fcis-31893	54	36	,	,	PUNCT
fcis-31893	54	37	which	which	PRON
fcis-31893	54	38	illustrates	illustrate	VERB
fcis-31893	54	39	the	the	DET
fcis-31893	54	40	complete	complete	ADJ
fcis-31893	54	41	model	model	NOUN
fcis-31893	54	42	structure	structure	NOUN
fcis-31893	54	43	.	.	PUNCT
fcis-31893	55	1	the	the	DET
fcis-31893	55	2	entire	entire	ADJ
fcis-31893	55	3	model	model	NOUN
fcis-31893	55	4	flowchart	flowchart	NOUN
fcis-31893	55	5	mainly	mainly	ADV
fcis-31893	55	6	consists	consist	VERB
fcis-31893	55	7	of	of	ADP
fcis-31893	55	8	4	4	NUM
fcis-31893	55	9	modules	module	NOUN
fcis-31893	55	10	,	,	PUNCT
fcis-31893	55	11	each	each	PRON
fcis-31893	55	12	with	with	ADP
fcis-31893	55	13	the	the	DET
fcis-31893	55	14	following	follow	VERB
fcis-31893	55	15	functions	function	NOUN
fcis-31893	55	16	and	and	CCONJ
fcis-31893	55	17	submodules	submodule	NOUN
fcis-31893	55	18	,	,	PUNCT
fcis-31893	55	19	(	(	PUNCT
fcis-31893	55	20	1	1	X
fcis-31893	55	21	)	)	PUNCT
fcis-31893	55	22	initial	initial	ADJ
fcis-31893	55	23	convolution	convolution	NOUN
fcis-31893	55	24	module	module	NOUN
fcis-31893	55	25	:	:	PUNCT
fcis-31893	55	26	convolution	convolution	NOUN
fcis-31893	55	27	layer	layer	NOUN
fcis-31893	55	28	conv1	conv1	NOUN
fcis-31893	55	29	,	,	PUNCT
fcis-31893	55	30	batch	batch	VERB
fcis-31893	55	31	normalization	normalization	NOUN
fcis-31893	55	32	batchnorm	batchnorm	NOUN
fcis-31893	55	33	,	,	PUNCT
fcis-31893	55	34	activation	activation	NOUN
fcis-31893	55	35	function	function	NOUN
fcis-31893	55	36	.	.	PUNCT
fcis-31893	56	1	(	(	PUNCT
fcis-31893	56	2	2	2	X
fcis-31893	56	3	)	)	PUNCT
fcis-31893	56	4	backbone	backbone	NOUN
fcis-31893	56	5	network	network	NOUN
fcis-31893	56	6	module	module	NOUN
fcis-31893	56	7	:	:	PUNCT
fcis-31893	56	8	residual	residual	ADJ
fcis-31893	56	9	block	block	NOUN
fcis-31893	56	10	,	,	PUNCT
fcis-31893	56	11	frequency	frequency	NOUN
fcis-31893	56	12	domain	domain	NOUN
fcis-31893	56	13	enhancement	enhancement	NOUN
fcis-31893	56	14	eca	eca	NOUN
fcis-31893	56	15	attention	attention	NOUN
fcis-31893	56	16	mechanism	mechanism	NOUN
fcis-31893	56	17	,	,	PUNCT
fcis-31893	56	18	downsampling	downsample	VERB
fcis-31893	56	19	.	.	PUNCT
fcis-31893	57	1	(	(	PUNCT
fcis-31893	57	2	3	3	X
fcis-31893	57	3	)	)	PUNCT
fcis-31893	57	4	enhanced	enhance	VERB
fcis-31893	57	5	frequency	frequency	NOUN
fcis-31893	57	6	eca	eca	NOUN
fcis-31893	57	7	attention	attention	NOUN
fcis-31893	57	8	module	module	NOUN
fcis-31893	57	9	:	:	PUNCT
fcis-31893	57	10	fft	fft	PROPN
fcis-31893	57	11	,	,	PUNCT
fcis-31893	57	12	multi	multi	ADJ
fcis-31893	57	13	-	-	ADJ
fcis-31893	57	14	scale	scale	ADJ
fcis-31893	57	15	convolution	convolution	NOUN
fcis-31893	57	16	,	,	PUNCT
fcis-31893	57	17	phase	phase	NOUN
fcis-31893	57	18	enhancement	enhancement	NOUN
fcis-31893	57	19	,	,	PUNCT
fcis-31893	57	20	sigmoid	sigmoid	NOUN
fcis-31893	57	21	activation	activation	NOUN
fcis-31893	57	22	.	.	PUNCT
fcis-31893	58	1	(	(	PUNCT
fcis-31893	58	2	4	4	X
fcis-31893	58	3	)	)	PUNCT
fcis-31893	58	4	pooling	pool	VERB
fcis-31893	58	5	module	module	NOUN
fcis-31893	58	6	:	:	PUNCT
fcis-31893	58	7	adaptive	adaptive	ADJ
fcis-31893	58	8	global	global	ADJ
fcis-31893	58	9	pooling	pool	VERB
fcis-31893	58	10	asp	asp	PROPN
fcis-31893	58	11	.	.	PROPN
fcis-31893	58	12	41	41	NUM
fcis-31893	58	13	fig	fig	NOUN
fcis-31893	58	14	4	4	NUM
fcis-31893	58	15	.	.	PUNCT
fcis-31893	58	16	convnext	convnext	PROPN
fcis-31893	58	17	-	-	PUNCT
fcis-31893	58	18	feca	feca	PROPN
fcis-31893	58	19	model	model	NOUN
fcis-31893	58	20	structure	structure	NOUN
fcis-31893	58	21	2.2	2.2	NUM
fcis-31893	58	22	.	.	PUNCT
fcis-31893	59	1	initial	initial	ADJ
fcis-31893	59	2	convolution	convolution	NOUN
fcis-31893	59	3	module	module	NOUN
fcis-31893	59	4	the	the	DET
fcis-31893	59	5	initial	initial	ADJ
fcis-31893	59	6	convolution	convolution	NOUN
fcis-31893	59	7	module	module	NOUN
fcis-31893	59	8	performs	perform	VERB
fcis-31893	59	9	primary	primary	ADJ
fcis-31893	59	10	feature	feature	NOUN
fcis-31893	59	11	extraction	extraction	NOUN
fcis-31893	59	12	on	on	ADP
fcis-31893	59	13	the	the	DET
fcis-31893	59	14	input	input	NOUN
fcis-31893	59	15	through	through	ADP
fcis-31893	59	16	a	a	DET
fcis-31893	59	17	3×3	3×3	NUM
fcis-31893	59	18	convolution	convolution	NOUN
fcis-31893	59	19	layer	layer	NOUN
fcis-31893	59	20	,	,	PUNCT
fcis-31893	59	21	and	and	CCONJ
fcis-31893	59	22	achieves	achieve	VERB
fcis-31893	59	23	normalization	normalization	NOUN
fcis-31893	59	24	and	and	CCONJ
fcis-31893	59	25	nonlinear	nonlinear	ADJ
fcis-31893	59	26	mapping	mapping	NOUN
fcis-31893	59	27	of	of	ADP
fcis-31893	59	28	the	the	DET
fcis-31893	59	29	feature	feature	NOUN
fcis-31893	59	30	space	space	NOUN
fcis-31893	59	31	through	through	ADP
fcis-31893	59	32	batch	batch	NOUN
fcis-31893	59	33	normalization	normalization	NOUN
fcis-31893	59	34	(	(	PUNCT
fcis-31893	59	35	bn	bn	NOUN
fcis-31893	59	36	)	)	PUNCT
fcis-31893	59	37	and	and	CCONJ
fcis-31893	59	38	the	the	DET
fcis-31893	59	39	nonlinear	nonlinear	ADJ
fcis-31893	59	40	activation	activation	NOUN
fcis-31893	59	41	function	function	NOUN
fcis-31893	59	42	relu	relu	NOUN
fcis-31893	59	43	.	.	PUNCT
fcis-31893	60	1	the	the	DET
fcis-31893	60	2	convolution	convolution	NOUN
fcis-31893	60	3	operation	operation	NOUN
fcis-31893	60	4	can	can	AUX
fcis-31893	60	5	capture	capture	VERB
fcis-31893	60	6	local	local	ADJ
fcis-31893	60	7	time	time	NOUN
fcis-31893	60	8	-	-	PUNCT
fcis-31893	60	9	frequency	frequency	NOUN
fcis-31893	60	10	features	feature	NOUN
fcis-31893	60	11	,	,	PUNCT
fcis-31893	60	12	generating	generate	VERB
fcis-31893	60	13	preliminary	preliminary	ADJ
fcis-31893	60	14	feature	feature	NOUN
fcis-31893	60	15	representations	representation	NOUN
fcis-31893	60	16	.	.	PUNCT
fcis-31893	61	1	batch	batch	NOUN
fcis-31893	61	2	normalization	normalization	NOUN
fcis-31893	61	3	alleviates	alleviate	VERB
fcis-31893	61	4	the	the	DET
fcis-31893	61	5	internal	internal	ADJ
fcis-31893	61	6	covariate	covariate	ADJ
fcis-31893	61	7	shift	shift	NOUN
fcis-31893	61	8	problem	problem	NOUN
fcis-31893	61	9	through	through	ADP
fcis-31893	61	10	normalization	normalization	NOUN
fcis-31893	61	11	,	,	PUNCT
fcis-31893	61	12	accelerating	accelerate	VERB
fcis-31893	61	13	network	network	NOUN
fcis-31893	61	14	convergence	convergence	NOUN
fcis-31893	61	15	.	.	PUNCT
fcis-31893	62	1	the	the	DET
fcis-31893	62	2	use	use	NOUN
fcis-31893	62	3	of	of	ADP
fcis-31893	62	4	relu	relu	NOUN
fcis-31893	62	5	introduces	introduce	NOUN
fcis-31893	62	6	nonlinearity	nonlinearity	NOUN
fcis-31893	62	7	,	,	PUNCT
fcis-31893	62	8	enhancing	enhance	VERB
fcis-31893	62	9	feature	feature	NOUN
fcis-31893	62	10	representation	representation	NOUN
fcis-31893	62	11	.	.	PUNCT
fcis-31893	63	1	this	this	DET
fcis-31893	63	2	module	module	NOUN
fcis-31893	63	3	provides	provide	VERB
fcis-31893	63	4	high	high	ADJ
fcis-31893	63	5	-	-	PUNCT
fcis-31893	63	6	quality	quality	NOUN
fcis-31893	63	7	initial	initial	ADJ
fcis-31893	63	8	feature	feature	NOUN
fcis-31893	63	9	representations	representation	NOUN
fcis-31893	63	10	for	for	ADP
fcis-31893	63	11	subsequent	subsequent	ADJ
fcis-31893	63	12	network	network	NOUN
fcis-31893	63	13	layers	layer	NOUN
fcis-31893	63	14	and	and	CCONJ
fcis-31893	63	15	enhances	enhance	VERB
fcis-31893	63	16	the	the	DET
fcis-31893	63	17	stability	stability	NOUN
fcis-31893	63	18	and	and	CCONJ
fcis-31893	63	19	resolution	resolution	NOUN
fcis-31893	63	20	of	of	ADP
fcis-31893	63	21	feature	feature	NOUN
fcis-31893	63	22	distribution	distribution	NOUN
fcis-31893	63	23	through	through	ADP
fcis-31893	63	24	normalization	normalization	NOUN
fcis-31893	63	25	and	and	CCONJ
fcis-31893	63	26	activation	activation	NOUN
fcis-31893	63	27	.	.	PUNCT
fcis-31893	64	1	2.3	2.3	NUM
fcis-31893	64	2	.	.	PUNCT
fcis-31893	64	3	backbone	backbone	NOUN
fcis-31893	64	4	network	network	NOUN
fcis-31893	64	5	module	module	NOUN
fcis-31893	64	6	the	the	DET
fcis-31893	64	7	backbone	backbone	NOUN
fcis-31893	64	8	network	network	NOUN
fcis-31893	64	9	module	module	NOUN
fcis-31893	64	10	realizes	realize	VERB
fcis-31893	64	11	multi	multi	ADJ
fcis-31893	64	12	-	-	ADJ
fcis-31893	64	13	scale	scale	ADJ
fcis-31893	64	14	feature	feature	NOUN
fcis-31893	64	15	extraction	extraction	NOUN
fcis-31893	64	16	and	and	CCONJ
fcis-31893	64	17	downsampling	downsample	VERB
fcis-31893	64	18	through	through	ADP
fcis-31893	64	19	the	the	DET
fcis-31893	64	20	stacking	stacking	NOUN
fcis-31893	64	21	of	of	ADP
fcis-31893	64	22	four	four	NUM
fcis-31893	64	23	levels	level	NOUN
fcis-31893	64	24	(	(	PUNCT
fcis-31893	64	25	layer	layer	NOUN
fcis-31893	64	26	1	1	NUM
fcis-31893	64	27	to	to	ADP
fcis-31893	64	28	layer	layer	NOUN
fcis-31893	64	29	4	4	NUM
fcis-31893	64	30	)	)	PUNCT
fcis-31893	64	31	as	as	SCONJ
fcis-31893	64	32	shown	show	VERB
fcis-31893	64	33	in	in	ADP
fcis-31893	64	34	figure	figure	NOUN
fcis-31893	64	35	5	5	NUM
fcis-31893	64	36	.	.	PUNCT
fcis-31893	65	1	each	each	DET
fcis-31893	65	2	level	level	NOUN
fcis-31893	65	3	consists	consist	VERB
fcis-31893	65	4	of	of	ADP
fcis-31893	65	5	multiple	multiple	ADJ
fcis-31893	65	6	residual	residual	ADJ
fcis-31893	65	7	blocks	block	NOUN
fcis-31893	65	8	to	to	PART
fcis-31893	65	9	mitigate	mitigate	VERB
fcis-31893	65	10	the	the	DET
fcis-31893	65	11	gradient	gradient	NOUN
fcis-31893	65	12	vanishing	vanishing	NOUN
fcis-31893	65	13	problem	problem	NOUN
fcis-31893	65	14	and	and	CCONJ
fcis-31893	65	15	enhance	enhance	VERB
fcis-31893	65	16	the	the	DET
fcis-31893	65	17	effective	effective	ADJ
fcis-31893	65	18	utilization	utilization	NOUN
fcis-31893	65	19	of	of	ADP
fcis-31893	65	20	network	network	NOUN
fcis-31893	65	21	depth[5	depth[5	PROPN
fcis-31893	65	22	]	]	PUNCT
fcis-31893	65	23	.	.	PUNCT
fcis-31893	66	1	design	design	NOUN
fcis-31893	66	2	of	of	ADP
fcis-31893	66	3	residual	residual	ADJ
fcis-31893	66	4	blocks	block	NOUN
fcis-31893	66	5	:	:	PUNCT
fcis-31893	66	6	each	each	DET
fcis-31893	66	7	residual	residual	ADJ
fcis-31893	66	8	block	block	NOUN
fcis-31893	66	9	integrates	integrate	VERB
fcis-31893	66	10	two	two	NUM
fcis-31893	66	11	convolutional	convolutional	ADJ
fcis-31893	66	12	layers	layer	NOUN
fcis-31893	66	13	,	,	PUNCT
fcis-31893	66	14	batch	batch	NOUN
fcis-31893	66	15	normalization	normalization	NOUN
fcis-31893	66	16	,	,	PUNCT
fcis-31893	66	17	and	and	CCONJ
fcis-31893	66	18	relu	relu	NOUN
fcis-31893	66	19	activation	activation	NOUN
fcis-31893	66	20	,	,	PUNCT
fcis-31893	66	21	while	while	SCONJ
fcis-31893	66	22	introducing	introduce	VERB
fcis-31893	66	23	the	the	DET
fcis-31893	66	24	frequency	frequency	NOUN
fcis-31893	66	25	domain	domain	NOUN
fcis-31893	66	26	enhanced	enhance	VERB
fcis-31893	66	27	eca	eca	NOUN
fcis-31893	66	28	attention	attention	NOUN
fcis-31893	66	29	mechanism	mechanism	NOUN
fcis-31893	66	30	to	to	PART
fcis-31893	66	31	optimize	optimize	VERB
fcis-31893	66	32	feature	feature	NOUN
fcis-31893	66	33	weights	weight	NOUN
fcis-31893	66	34	.	.	PUNCT
fcis-31893	67	1	downsampling	downsample	VERB
fcis-31893	67	2	gradually	gradually	ADV
fcis-31893	67	3	reduces	reduce	VERB
fcis-31893	67	4	the	the	DET
fcis-31893	67	5	size	size	NOUN
fcis-31893	67	6	of	of	ADP
fcis-31893	67	7	the	the	DET
fcis-31893	67	8	feature	feature	NOUN
fcis-31893	67	9	maps	map	NOUN
fcis-31893	67	10	through	through	ADP
fcis-31893	67	11	stride	stride	ADJ
fcis-31893	67	12	adjustments	adjustment	NOUN
fcis-31893	67	13	between	between	ADP
fcis-31893	67	14	levels	level	NOUN
fcis-31893	67	15	,	,	PUNCT
fcis-31893	67	16	enhancing	enhance	VERB
fcis-31893	67	17	the	the	DET
fcis-31893	67	18	model	model	NOUN
fcis-31893	67	19	's	's	PART
fcis-31893	67	20	time	time	NOUN
fcis-31893	67	21	-	-	PUNCT
fcis-31893	67	22	frequency	frequency	NOUN
fcis-31893	67	23	resolution	resolution	NOUN
fcis-31893	67	24	capabilities	capability	NOUN
fcis-31893	67	25	.	.	PUNCT
fcis-31893	68	1	effective	effective	ADJ
fcis-31893	68	2	gradient	gradient	ADJ
fcis-31893	68	3	propagation	propagation	NOUN
fcis-31893	68	4	and	and	CCONJ
fcis-31893	68	5	deep	deep	ADJ
fcis-31893	68	6	feature	feature	NOUN
fcis-31893	68	7	learning	learning	NOUN
fcis-31893	68	8	are	be	AUX
fcis-31893	68	9	achieved	achieve	VERB
fcis-31893	68	10	through	through	ADP
fcis-31893	68	11	the	the	DET
fcis-31893	68	12	residual	residual	ADJ
fcis-31893	68	13	structure	structure	NOUN
fcis-31893	68	14	.	.	PUNCT
fcis-31893	69	1	the	the	DET
fcis-31893	69	2	enhanced	enhanced	ADJ
fcis-31893	69	3	frequency	frequency	NOUN
fcis-31893	69	4	eca	eca	NOUN
fcis-31893	69	5	attention	attention	NOUN
fcis-31893	69	6	mechanism	mechanism	NOUN
fcis-31893	69	7	is	be	AUX
fcis-31893	69	8	introduced	introduce	VERB
fcis-31893	69	9	during	during	ADP
fcis-31893	69	10	the	the	DET
fcis-31893	69	11	feature	feature	NOUN
fcis-31893	69	12	extraction	extraction	NOUN
fcis-31893	69	13	process	process	NOUN
fcis-31893	69	14	.	.	PUNCT
fcis-31893	70	1	fig	fig	NOUN
fcis-31893	70	2	5	5	NUM
fcis-31893	70	3	.	.	PUNCT
fcis-31893	70	4	main	main	ADJ
fcis-31893	70	5	network	network	NOUN
fcis-31893	70	6	module	module	NOUN
fcis-31893	70	7	2.4	2.4	NUM
fcis-31893	70	8	.	.	PUNCT
fcis-31893	71	1	enhanced	enhance	VERB
fcis-31893	71	2	frequency	frequency	NOUN
fcis-31893	71	3	eca	eca	NOUN
fcis-31893	71	4	attention	attention	NOUN
fcis-31893	71	5	module	module	NOUN
fcis-31893	71	6	this	this	DET
fcis-31893	71	7	module	module	NOUN
fcis-31893	71	8	is	be	AUX
fcis-31893	71	9	centered	center	VERB
fcis-31893	71	10	on	on	ADP
fcis-31893	71	11	frequency	frequency	NOUN
fcis-31893	71	12	domain	domain	NOUN
fcis-31893	71	13	operations	operation	NOUN
fcis-31893	71	14	,	,	PUNCT
fcis-31893	71	15	integrating	integrate	VERB
fcis-31893	71	16	multi	multi	ADJ
fcis-31893	71	17	-	-	ADJ
fcis-31893	71	18	scale	scale	ADJ
fcis-31893	71	19	convolution	convolution	NOUN
fcis-31893	71	20	and	and	CCONJ
fcis-31893	71	21	phase	phase	NOUN
fcis-31893	71	22	information	information	NOUN
fcis-31893	71	23	to	to	PART
fcis-31893	71	24	enhance	enhance	VERB
fcis-31893	71	25	the	the	DET
fcis-31893	71	26	precision	precision	NOUN
fcis-31893	71	27	of	of	ADP
fcis-31893	71	28	channel	channel	NOUN
fcis-31893	71	29	weight	weight	NOUN
fcis-31893	71	30	calculations	calculation	NOUN
fcis-31893	71	31	as	as	SCONJ
fcis-31893	71	32	shown	show	VERB
fcis-31893	71	33	in	in	ADP
fcis-31893	71	34	figure	figure	NOUN
fcis-31893	71	35	6	6	NUM
fcis-31893	71	36	.	.	PUNCT
fcis-31893	72	1	the	the	DET
fcis-31893	72	2	frequency	frequency	NOUN
fcis-31893	72	3	domain	domain	NOUN
fcis-31893	72	4	transformation	transformation	NOUN
fcis-31893	72	5	utilizes	utilize	VERB
fcis-31893	72	6	fast	fast	ADJ
fcis-31893	72	7	fourier	fourier	NOUN
fcis-31893	72	8	transform	transform	NOUN
fcis-31893	72	9	(	(	PUNCT
fcis-31893	72	10	fft)[6	fft)[6	NOUN
fcis-31893	72	11	]	]	PUNCT
fcis-31893	72	12	to	to	PART
fcis-31893	72	13	map	map	VERB
fcis-31893	72	14	feature	feature	NOUN
fcis-31893	72	15	maps	map	NOUN
fcis-31893	72	16	from	from	ADP
fcis-31893	72	17	the	the	DET
fcis-31893	72	18	spatial	spatial	ADJ
fcis-31893	72	19	domain	domain	NOUN
fcis-31893	72	20	to	to	ADP
fcis-31893	72	21	the	the	DET
fcis-31893	72	22	frequency	frequency	NOUN
fcis-31893	72	23	domain	domain	NOUN
fcis-31893	72	24	,	,	PUNCT
fcis-31893	72	25	separating	separate	VERB
fcis-31893	72	26	magnitude	magnitude	NOUN
fcis-31893	72	27	and	and	CCONJ
fcis-31893	72	28	phase	phase	NOUN
fcis-31893	72	29	information	information	NOUN
fcis-31893	72	30	.	.	PUNCT
fcis-31893	73	1	multi	multi	ADJ
fcis-31893	73	2	-	-	ADJ
fcis-31893	73	3	scale	scale	ADJ
fcis-31893	73	4	convolution	convolution	NOUN
fcis-31893	73	5	processes	process	NOUN
fcis-31893	73	6	frequency	frequency	NOUN
fcis-31893	73	7	domain	domain	NOUN
fcis-31893	73	8	magnitude	magnitude	NOUN
fcis-31893	73	9	information	information	NOUN
fcis-31893	73	10	using	use	VERB
fcis-31893	73	11	convolution	convolution	NOUN
fcis-31893	73	12	kernels	kernel	NOUN
fcis-31893	73	13	of	of	ADP
fcis-31893	73	14	different	different	ADJ
fcis-31893	73	15	scales	scale	NOUN
fcis-31893	73	16	to	to	PART
fcis-31893	73	17	capture	capture	VERB
fcis-31893	73	18	features	feature	NOUN
fcis-31893	73	19	across	across	ADP
fcis-31893	73	20	multiple	multiple	ADJ
fcis-31893	73	21	frequency	frequency	NOUN
fcis-31893	73	22	ranges	range	NOUN
fcis-31893	73	23	.	.	PUNCT
fcis-31893	74	1	phase	phase	NOUN
fcis-31893	74	2	enhancement	enhancement	NOUN
fcis-31893	74	3	involves	involve	VERB
fcis-31893	74	4	enabling	enable	VERB
fcis-31893	74	5	phase	phase	NOUN
fcis-31893	74	6	processing	processing	NOUN
fcis-31893	74	7	and	and	CCONJ
fcis-31893	74	8	considering	consider	VERB
fcis-31893	74	9	phase	phase	NOUN
fcis-31893	74	10	information	information	NOUN
fcis-31893	74	11	during	during	ADP
fcis-31893	74	12	the	the	DET
fcis-31893	74	13	reconstruction	reconstruction	NOUN
fcis-31893	74	14	of	of	ADP
fcis-31893	74	15	feature	feature	NOUN
fcis-31893	74	16	maps	map	NOUN
fcis-31893	74	17	to	to	PART
fcis-31893	74	18	enhance	enhance	VERB
fcis-31893	74	19	feature	feature	NOUN
fcis-31893	74	20	expressive	expressive	ADJ
fcis-31893	74	21	capability	capability	NOUN
fcis-31893	74	22	.	.	PUNCT
fcis-31893	75	1	inverse	inverse	NOUN
fcis-31893	75	2	transformation	transformation	NOUN
fcis-31893	75	3	uses	use	VERB
fcis-31893	75	4	inverse	inverse	NOUN
fcis-31893	75	5	fast	fast	ADJ
fcis-31893	75	6	fourier	fourier	NOUN
fcis-31893	75	7	transform	transform	NOUN
fcis-31893	75	8	(	(	PUNCT
fcis-31893	75	9	ifft	ifft	PROPN
fcis-31893	75	10	)	)	PUNCT
fcis-31893	75	11	to	to	PART
fcis-31893	75	12	map	map	VERB
fcis-31893	75	13	the	the	DET
fcis-31893	75	14	processed	process	VERB
fcis-31893	75	15	frequency	frequency	NOUN
fcis-31893	75	16	domain	domain	NOUN
fcis-31893	75	17	information	information	NOUN
fcis-31893	75	18	back	back	ADV
fcis-31893	75	19	to	to	ADP
fcis-31893	75	20	the	the	DET
fcis-31893	75	21	spatial	spatial	ADJ
fcis-31893	75	22	domain	domain	NOUN
fcis-31893	75	23	.	.	PUNCT
fcis-31893	76	1	activation	activation	NOUN
fcis-31893	76	2	and	and	CCONJ
fcis-31893	76	3	channel	channel	NOUN
fcis-31893	76	4	weight	weight	NOUN
fcis-31893	76	5	calculation	calculation	NOUN
fcis-31893	76	6	generate	generate	VERB
fcis-31893	76	7	attention	attention	NOUN
fcis-31893	76	8	weights	weight	NOUN
fcis-31893	76	9	through	through	ADP
fcis-31893	76	10	sigmoid	sigmoid	NOUN
fcis-31893	76	11	activation	activation	NOUN
fcis-31893	76	12	and	and	CCONJ
fcis-31893	76	13	dynamically	dynamically	ADV
fcis-31893	76	14	adjust	adjust	VERB
fcis-31893	76	15	channel	channel	NOUN
fcis-31893	76	16	features	feature	NOUN
fcis-31893	76	17	.	.	PUNCT
fcis-31893	77	1	this	this	DET
fcis-31893	77	2	module	module	NOUN
fcis-31893	77	3	effectively	effectively	ADV
fcis-31893	77	4	combines	combine	VERB
fcis-31893	77	5	information	information	NOUN
fcis-31893	77	6	from	from	ADP
fcis-31893	77	7	both	both	CCONJ
fcis-31893	77	8	the	the	DET
fcis-31893	77	9	frequency	frequency	NOUN
fcis-31893	77	10	and	and	CCONJ
fcis-31893	77	11	spatial	spatial	ADJ
fcis-31893	77	12	domains	domain	NOUN
fcis-31893	77	13	,	,	PUNCT
fcis-31893	77	14	introducing	introduce	VERB
fcis-31893	77	15	multi	multi	ADJ
fcis-31893	77	16	-	-	ADJ
fcis-31893	77	17	scale	scale	ADJ
fcis-31893	77	18	perception	perception	NOUN
fcis-31893	77	19	and	and	CCONJ
fcis-31893	77	20	phase	phase	NOUN
fcis-31893	77	21	enhancement	enhancement	NOUN
fcis-31893	77	22	to	to	PART
fcis-31893	77	23	improve	improve	VERB
fcis-31893	77	24	the	the	DET
fcis-31893	77	25	model	model	NOUN
fcis-31893	77	26	's	's	PART
fcis-31893	77	27	robustness	robustness	NOUN
fcis-31893	77	28	and	and	CCONJ
fcis-31893	77	29	sensitivity	sensitivity	NOUN
fcis-31893	77	30	to	to	ADP
fcis-31893	77	31	complex	complex	ADJ
fcis-31893	77	32	audio	audio	ADJ
fcis-31893	77	33	signals	signal	NOUN
fcis-31893	77	34	.	.	PUNCT
fcis-31893	78	1	2.5	2.5	NUM
fcis-31893	78	2	.	.	PUNCT
fcis-31893	78	3	pooling	pool	VERB
fcis-31893	78	4	module	module	NOUN
fcis-31893	78	5	the	the	DET
fcis-31893	78	6	pooling	pooling	NOUN
fcis-31893	78	7	module	module	NOUN
fcis-31893	78	8	is	be	AUX
fcis-31893	78	9	shown	show	VERB
fcis-31893	78	10	in	in	ADP
fcis-31893	78	11	figure	figure	NOUN
fcis-31893	78	12	7	7	NUM
fcis-31893	78	13	.	.	PUNCT
fcis-31893	78	14	through	through	ADP
fcis-31893	78	15	adaptive	adaptive	ADJ
fcis-31893	78	16	average	average	ADJ
fcis-31893	78	17	pooling	pooling	NOUN
fcis-31893	78	18	(	(	PUNCT
fcis-31893	78	19	asp)[7	asp)[7	PROPN
fcis-31893	78	20	]	]	PUNCT
fcis-31893	78	21	,	,	PUNCT
fcis-31893	78	22	it	it	PRON
fcis-31893	78	23	maps	map	VERB
fcis-31893	78	24	the	the	DET
fcis-31893	78	25	feature	feature	NOUN
fcis-31893	78	26	of	of	ADP
fcis-31893	78	27	each	each	DET
fcis-31893	78	28	channel	channel	NOUN
fcis-31893	78	29	to	to	ADP
fcis-31893	78	30	a	a	DET
fcis-31893	78	31	single	single	ADJ
fcis-31893	78	32	value	value	NOUN
fcis-31893	78	33	,	,	PUNCT
fcis-31893	78	34	significantly	significantly	ADV
fcis-31893	78	35	reducing	reduce	VERB
fcis-31893	78	36	computational	computational	ADJ
fcis-31893	78	37	complexity	complexity	NOUN
fcis-31893	78	38	and	and	CCONJ
fcis-31893	78	39	enhancing	enhance	VERB
fcis-31893	78	40	the	the	DET
fcis-31893	78	41	global	global	ADJ
fcis-31893	78	42	representation	representation	NOUN
fcis-31893	78	43	of	of	ADP
fcis-31893	78	44	features	feature	NOUN
fcis-31893	78	45	.	.	PUNCT
fcis-31893	79	1	the	the	DET
fcis-31893	79	2	pooling	pool	VERB
fcis-31893	79	3	operation	operation	NOUN
fcis-31893	79	4	:	:	PUNCT
fcis-31893	79	5	by	by	ADP
fcis-31893	79	6	global	global	ADJ
fcis-31893	79	7	42	42	NUM
fcis-31893	79	8	aggregation	aggregation	NOUN
fcis-31893	79	9	,	,	PUNCT
fcis-31893	79	10	ensures	ensure	VERB
fcis-31893	79	11	that	that	SCONJ
fcis-31893	79	12	important	important	ADJ
fcis-31893	79	13	information	information	NOUN
fcis-31893	79	14	is	be	AUX
fcis-31893	79	15	preserved	preserve	VERB
fcis-31893	79	16	in	in	ADP
fcis-31893	79	17	each	each	DET
fcis-31893	79	18	channel	channel	NOUN
fcis-31893	79	19	's	's	PART
fcis-31893	79	20	features	feature	NOUN
fcis-31893	79	21	during	during	ADP
fcis-31893	79	22	the	the	DET
fcis-31893	79	23	dimensionality	dimensionality	NOUN
fcis-31893	79	24	reduction	reduction	NOUN
fcis-31893	79	25	process	process	NOUN
fcis-31893	79	26	.	.	PUNCT
fcis-31893	80	1	adaptive	adaptive	ADJ
fcis-31893	80	2	pooling	pooling	NOUN
fcis-31893	80	3	accommodates	accommodate	VERB
fcis-31893	80	4	different	different	ADJ
fcis-31893	80	5	input	input	NOUN
fcis-31893	80	6	sizes	size	NOUN
fcis-31893	80	7	and	and	CCONJ
fcis-31893	80	8	ensures	ensure	VERB
fcis-31893	80	9	that	that	SCONJ
fcis-31893	80	10	the	the	DET
fcis-31893	80	11	network	network	NOUN
fcis-31893	80	12	maintains	maintain	VERB
fcis-31893	80	13	consistent	consistent	ADJ
fcis-31893	80	14	performance	performance	NOUN
fcis-31893	80	15	when	when	SCONJ
fcis-31893	80	16	processing	process	VERB
fcis-31893	80	17	audio	audio	NOUN
fcis-31893	80	18	data	datum	NOUN
fcis-31893	80	19	of	of	ADP
fcis-31893	80	20	various	various	ADJ
fcis-31893	80	21	scales	scale	NOUN
fcis-31893	80	22	.	.	PUNCT
fcis-31893	81	1	the	the	DET
fcis-31893	81	2	output	output	NOUN
fcis-31893	81	3	features	feature	VERB
fcis-31893	81	4	from	from	ADP
fcis-31893	81	5	layer	layer	NOUN
fcis-31893	81	6	4	4	NUM
fcis-31893	81	7	are	be	AUX
fcis-31893	81	8	used	use	VERB
fcis-31893	81	9	as	as	ADP
fcis-31893	81	10	input	input	NOUN
fcis-31893	81	11	,	,	PUNCT
fcis-31893	81	12	which	which	PRON
fcis-31893	81	13	then	then	ADV
fcis-31893	81	14	undergoes	undergo	VERB
fcis-31893	81	15	adaptive	adaptive	ADJ
fcis-31893	81	16	global	global	ADJ
fcis-31893	81	17	pooling	pooling	NOUN
fcis-31893	81	18	,	,	PUNCT
fcis-31893	81	19	flattening	flattening	NOUN
fcis-31893	81	20	of	of	ADP
fcis-31893	81	21	features	feature	NOUN
fcis-31893	81	22	,	,	PUNCT
fcis-31893	81	23	and	and	CCONJ
fcis-31893	81	24	a	a	DET
fcis-31893	81	25	fully	fully	ADV
fcis-31893	81	26	connected	connect	VERB
fcis-31893	81	27	layer	layer	NOUN
fcis-31893	81	28	(	(	PUNCT
fcis-31893	81	29	fc	fc	INTJ
fcis-31893	81	30	)	)	PUNCT
fcis-31893	81	31	to	to	PART
fcis-31893	81	32	map	map	VERB
fcis-31893	81	33	the	the	DET
fcis-31893	81	34	features	feature	NOUN
fcis-31893	81	35	into	into	ADP
fcis-31893	81	36	the	the	DET
fcis-31893	81	37	classification	classification	NOUN
fcis-31893	81	38	target	target	NOUN
fcis-31893	81	39	space	space	NOUN
fcis-31893	81	40	,	,	PUNCT
fcis-31893	81	41	ultimately	ultimately	ADV
fcis-31893	81	42	outputting	output	VERB
fcis-31893	81	43	the	the	DET
fcis-31893	81	44	classification	classification	NOUN
fcis-31893	81	45	results	result	NOUN
fcis-31893	81	46	.	.	PUNCT
fcis-31893	82	1	fig	fig	NOUN
fcis-31893	82	2	6	6	NUM
fcis-31893	82	3	.	.	PUNCT
fcis-31893	82	4	enhanced	enhance	VERB
fcis-31893	82	5	frequency	frequency	NOUN
fcis-31893	82	6	eca	eca	NOUN
fcis-31893	82	7	attention	attention	NOUN
fcis-31893	82	8	module	module	NOUN
fcis-31893	82	9	fig	fig	NOUN
fcis-31893	82	10	7	7	NUM
fcis-31893	82	11	.	.	PUNCT
fcis-31893	83	1	pooling	pool	VERB
fcis-31893	83	2	module	module	NOUN
fcis-31893	83	3	3	3	NUM
fcis-31893	83	4	.	.	PUNCT
fcis-31893	83	5	experimental	experimental	ADJ
fcis-31893	83	6	results	result	NOUN
fcis-31893	83	7	and	and	CCONJ
fcis-31893	83	8	analysis	analysis	NOUN
fcis-31893	83	9	3.1	3.1	NUM
fcis-31893	83	10	.	.	PUNCT
fcis-31893	83	11	experimental	experimental	ADJ
fcis-31893	83	12	environment	environment	NOUN
fcis-31893	83	13	and	and	CCONJ
fcis-31893	83	14	parameter	parameter	NOUN
fcis-31893	83	15	settings	setting	NOUN
fcis-31893	83	16	the	the	DET
fcis-31893	83	17	experiment	experiment	NOUN
fcis-31893	83	18	was	be	AUX
fcis-31893	83	19	carried	carry	VERB
fcis-31893	83	20	out	out	ADP
fcis-31893	83	21	using	use	VERB
fcis-31893	83	22	the	the	DET
fcis-31893	83	23	pytorch	pytorch	NOUN
fcis-31893	83	24	deep	deep	ADJ
fcis-31893	83	25	learning	learning	NOUN
fcis-31893	83	26	framework	framework	NOUN
fcis-31893	83	27	,	,	PUNCT
fcis-31893	83	28	with	with	ADP
fcis-31893	83	29	an	an	DET
fcis-31893	83	30	nvidia	nvidia	PROPN
fcis-31893	83	31	geforce	geforce	NOUN
fcis-31893	83	32	gtx	gtx	PROPN
fcis-31893	83	33	3080ti	3080ti	PROPN
fcis-31893	83	34	gpu	gpu	NOUN
fcis-31893	83	35	processor	processor	NOUN
fcis-31893	83	36	,	,	PUNCT
fcis-31893	83	37	12	12	NUM
fcis-31893	83	38	gb	gb	NOUN
fcis-31893	83	39	of	of	ADP
fcis-31893	83	40	memory	memory	NOUN
fcis-31893	83	41	,	,	PUNCT
fcis-31893	83	42	and	and	CCONJ
fcis-31893	83	43	the	the	DET
fcis-31893	83	44	ubuntu	ubuntu	ADJ
fcis-31893	83	45	20.04	20.04	NUM
fcis-31893	83	46	operating	operating	NOUN
fcis-31893	83	47	system	system	NOUN
fcis-31893	83	48	.	.	PUNCT
fcis-31893	84	1	in	in	ADP
fcis-31893	84	2	the	the	DET
fcis-31893	84	3	urbansound8k	urbansound8k	NOUN
fcis-31893	84	4	dataset	dataset	NOUN
fcis-31893	84	5	,	,	PUNCT
fcis-31893	84	6	80	80	NUM
fcis-31893	84	7	%	%	NOUN
fcis-31893	84	8	of	of	ADP
fcis-31893	84	9	the	the	DET
fcis-31893	84	10	audio	audio	NOUN
fcis-31893	84	11	was	be	AUX
fcis-31893	84	12	designated	designate	VERB
fcis-31893	84	13	as	as	ADP
fcis-31893	84	14	the	the	DET
fcis-31893	84	15	training	training	NOUN
fcis-31893	84	16	set	set	NOUN
fcis-31893	84	17	and	and	CCONJ
fcis-31893	84	18	20	20	NUM
fcis-31893	84	19	%	%	NOUN
fcis-31893	84	20	as	as	ADP
fcis-31893	84	21	the	the	DET
fcis-31893	84	22	test	test	NOUN
fcis-31893	84	23	set	set	NOUN
fcis-31893	84	24	.	.	PUNCT
fcis-31893	85	1	the	the	DET
fcis-31893	85	2	adaptive	adaptive	ADJ
fcis-31893	85	3	moment	moment	NOUN
fcis-31893	85	4	estimation	estimation	NOUN
fcis-31893	85	5	optimization	optimization	NOUN
fcis-31893	85	6	algorithm	algorithm	NOUN
fcis-31893	85	7	(	(	PUNCT
fcis-31893	85	8	adam	adam	PROPN
fcis-31893	85	9	)	)	PUNCT
fcis-31893	85	10	was	be	AUX
fcis-31893	85	11	chosen	choose	VERB
fcis-31893	85	12	for	for	ADP
fcis-31893	85	13	gradient	gradient	ADJ
fcis-31893	85	14	optimization	optimization	NOUN
fcis-31893	85	15	,	,	PUNCT
fcis-31893	85	16	with	with	ADP
fcis-31893	85	17	an	an	DET
fcis-31893	85	18	initial	initial	ADJ
fcis-31893	85	19	learning	learning	NOUN
fcis-31893	85	20	rate	rate	NOUN
fcis-31893	85	21	set	set	VERB
fcis-31893	85	22	to	to	ADP
fcis-31893	85	23	0.001	0.001	NUM
fcis-31893	85	24	and	and	CCONJ
fcis-31893	85	25	weight	weight	NOUN
fcis-31893	85	26	decay	decay	NOUN
fcis-31893	85	27	mechanisms	mechanism	NOUN
fcis-31893	85	28	implemented	implement	VERB
fcis-31893	85	29	to	to	PART
fcis-31893	85	30	mitigate	mitigate	VERB
fcis-31893	85	31	overfitting	overfitting	NOUN
fcis-31893	85	32	.	.	PUNCT
fcis-31893	86	1	the	the	DET
fcis-31893	86	2	training	training	NOUN
fcis-31893	86	3	consisted	consist	VERB
fcis-31893	86	4	of	of	ADP
fcis-31893	86	5	60	60	NUM
fcis-31893	86	6	epochs	epoch	NOUN
fcis-31893	86	7	with	with	ADP
fcis-31893	86	8	a	a	DET
fcis-31893	86	9	batch	batch	NOUN
fcis-31893	86	10	size	size	NOUN
fcis-31893	86	11	of	of	ADP
fcis-31893	86	12	32	32	NUM
fcis-31893	86	13	.	.	PUNCT
fcis-31893	87	1	this	this	DET
fcis-31893	87	2	paper	paper	NOUN
fcis-31893	87	3	employs	employ	VERB
fcis-31893	87	4	transfer	transfer	NOUN
fcis-31893	87	5	learning	learning	NOUN
fcis-31893	87	6	by	by	ADP
fcis-31893	87	7	using	use	VERB
fcis-31893	87	8	the	the	DET
fcis-31893	87	9	pre	pre	ADJ
fcis-31893	87	10	-	-	ADJ
fcis-31893	87	11	trained	train	VERB
fcis-31893	87	12	weights	weight	NOUN
fcis-31893	87	13	of	of	ADP
fcis-31893	87	14	the	the	DET
fcis-31893	87	15	convnext	convnext	ADJ
fcis-31893	87	16	network	network	NOUN
fcis-31893	87	17	on	on	ADP
fcis-31893	87	18	urbansound8k	urbansound8k	NOUN
fcis-31893	87	19	as	as	ADP
fcis-31893	87	20	the	the	DET
fcis-31893	87	21	initial	initial	ADJ
fcis-31893	87	22	weights	weight	NOUN
fcis-31893	87	23	for	for	ADP
fcis-31893	87	24	the	the	DET
fcis-31893	87	25	model	model	NOUN
fcis-31893	87	26	in	in	ADP
fcis-31893	87	27	this	this	DET
fcis-31893	87	28	study	study	NOUN
fcis-31893	87	29	.	.	PUNCT
fcis-31893	88	1	3.2	3.2	NUM
fcis-31893	88	2	.	.	PUNCT
fcis-31893	89	1	experimental	experimental	ADJ
fcis-31893	89	2	results	result	NOUN
fcis-31893	89	3	and	and	CCONJ
fcis-31893	89	4	analysis	analysis	NOUN
fcis-31893	89	5	according	accord	VERB
fcis-31893	89	6	to	to	ADP
fcis-31893	89	7	commonly	commonly	ADV
fcis-31893	89	8	used	use	VERB
fcis-31893	89	9	performance	performance	NOUN
fcis-31893	89	10	evaluation	evaluation	NOUN
fcis-31893	89	11	standards	standard	NOUN
fcis-31893	89	12	,	,	PUNCT
fcis-31893	89	13	the	the	PRON
fcis-31893	89	14	higher	high	ADJ
fcis-31893	89	15	the	the	DET
fcis-31893	89	16	accuracy	accuracy	NOUN
fcis-31893	89	17	of	of	ADP
fcis-31893	89	18	the	the	DET
fcis-31893	89	19	model	model	NOUN
fcis-31893	89	20	,	,	PUNCT
fcis-31893	89	21	the	the	PRON
fcis-31893	89	22	more	more	ADV
fcis-31893	89	23	precise	precise	ADJ
fcis-31893	89	24	its	its	PRON
fcis-31893	89	25	recognition	recognition	NOUN
fcis-31893	89	26	ability	ability	NOUN
fcis-31893	89	27	,	,	PUNCT
fcis-31893	89	28	allowing	allow	VERB
fcis-31893	89	29	for	for	ADP
fcis-31893	89	30	effective	effective	ADJ
fcis-31893	89	31	and	and	CCONJ
fcis-31893	89	32	accurate	accurate	ADJ
fcis-31893	89	33	classification	classification	NOUN
fcis-31893	89	34	of	of	ADP
fcis-31893	89	35	sounds	sound	NOUN
fcis-31893	89	36	in	in	ADP
fcis-31893	89	37	urban	urban	ADJ
fcis-31893	89	38	environments	environment	NOUN
fcis-31893	89	39	;	;	PUNCT
fcis-31893	89	40	a	a	DET
fcis-31893	89	41	lower	low	ADJ
fcis-31893	89	42	loss	loss	NOUN
fcis-31893	89	43	rate	rate	NOUN
fcis-31893	89	44	indicates	indicate	VERB
fcis-31893	89	45	that	that	SCONJ
fcis-31893	89	46	the	the	DET
fcis-31893	89	47	model	model	NOUN
fcis-31893	89	48	's	's	PART
fcis-31893	89	49	robustness	robustness	NOUN
fcis-31893	89	50	is	be	AUX
fcis-31893	89	51	relatively	relatively	ADV
fcis-31893	89	52	strong	strong	ADJ
fcis-31893	89	53	.	.	PUNCT
fcis-31893	90	1	during	during	ADP
fcis-31893	90	2	the	the	DET
fcis-31893	90	3	model	model	NOUN
fcis-31893	90	4	training	training	NOUN
fcis-31893	90	5	process	process	NOUN
fcis-31893	90	6	,	,	PUNCT
fcis-31893	90	7	the	the	DET
fcis-31893	90	8	comparison	comparison	NOUN
fcis-31893	90	9	of	of	ADP
fcis-31893	90	10	training	training	NOUN
fcis-31893	90	11	loss	loss	NOUN
fcis-31893	90	12	versus	versus	ADP
fcis-31893	90	13	evaluation	evaluation	NOUN
fcis-31893	90	14	loss	loss	NOUN
fcis-31893	90	15	and	and	CCONJ
fcis-31893	90	16	the	the	DET
fcis-31893	90	17	change	change	NOUN
fcis-31893	90	18	in	in	ADP
fcis-31893	90	19	training	training	NOUN
fcis-31893	90	20	accuracy	accuracy	NOUN
fcis-31893	90	21	versus	versus	ADP
fcis-31893	90	22	evaluation	evaluation	NOUN
fcis-31893	90	23	accuracy	accuracy	NOUN
fcis-31893	90	24	are	be	AUX
fcis-31893	90	25	shown	show	VERB
fcis-31893	90	26	in	in	ADP
fcis-31893	90	27	figures	figure	NOUN
fcis-31893	90	28	8	8	NUM
fcis-31893	90	29	and	and	CCONJ
fcis-31893	90	30	9	9	NUM
fcis-31893	90	31	.	.	PUNCT
fcis-31893	91	1	the	the	DET
fcis-31893	91	2	final	final	ADJ
fcis-31893	91	3	validation	validation	NOUN
fcis-31893	91	4	set	set	VERB
fcis-31893	91	5	accuracy	accuracy	NOUN
fcis-31893	91	6	and	and	CCONJ
fcis-31893	91	7	loss	loss	NOUN
fcis-31893	91	8	for	for	ADP
fcis-31893	91	9	this	this	DET
fcis-31893	91	10	experiment	experiment	NOUN
fcis-31893	91	11	are	be	AUX
fcis-31893	91	12	0.985	0.985	NUM
fcis-31893	91	13	and	and	CCONJ
fcis-31893	91	14	0.063	0.063	NUM
fcis-31893	91	15	,	,	PUNCT
fcis-31893	91	16	respectively	respectively	ADV
fcis-31893	91	17	,	,	PUNCT
fcis-31893	91	18	indicating	indicate	VERB
fcis-31893	91	19	good	good	ADJ
fcis-31893	91	20	convergence	convergence	NOUN
fcis-31893	91	21	of	of	ADP
fcis-31893	91	22	the	the	DET
fcis-31893	91	23	network	network	NOUN
fcis-31893	91	24	.	.	PUNCT
fcis-31893	92	1	during	during	ADP
fcis-31893	92	2	training	training	NOUN
fcis-31893	92	3	,	,	PUNCT
fcis-31893	92	4	the	the	DET
fcis-31893	92	5	training	training	NOUN
fcis-31893	92	6	loss	loss	NOUN
fcis-31893	92	7	shows	show	VERB
fcis-31893	92	8	a	a	DET
fcis-31893	92	9	gradual	gradual	ADJ
fcis-31893	92	10	decrease	decrease	NOUN
fcis-31893	92	11	,	,	PUNCT
fcis-31893	92	12	indicating	indicate	VERB
fcis-31893	92	13	that	that	SCONJ
fcis-31893	92	14	the	the	DET
fcis-31893	92	15	model	model	NOUN
fcis-31893	92	16	is	be	AUX
fcis-31893	92	17	continuously	continuously	ADV
fcis-31893	92	18	learning	learn	VERB
fcis-31893	92	19	and	and	CCONJ
fcis-31893	92	20	optimizing	optimize	VERB
fcis-31893	92	21	its	its	PRON
fcis-31893	92	22	parameters	parameter	NOUN
fcis-31893	92	23	.	.	PUNCT
fcis-31893	93	1	however	however	ADV
fcis-31893	93	2	,	,	PUNCT
fcis-31893	93	3	the	the	DET
fcis-31893	93	4	evaluation	evaluation	NOUN
fcis-31893	93	5	loss	loss	NOUN
fcis-31893	93	6	(	(	PUNCT
fcis-31893	93	7	validation	validation	NOUN
fcis-31893	93	8	loss	loss	NOUN
fcis-31893	93	9	)	)	PUNCT
fcis-31893	93	10	remains	remain	VERB
fcis-31893	93	11	at	at	ADP
fcis-31893	93	12	a	a	DET
fcis-31893	93	13	certain	certain	ADJ
fcis-31893	93	14	level	level	NOUN
fcis-31893	93	15	with	with	ADP
fcis-31893	93	16	slight	slight	ADJ
fcis-31893	93	17	fluctuations	fluctuation	NOUN
fcis-31893	93	18	.	.	PUNCT
fcis-31893	94	1	this	this	PRON
fcis-31893	94	2	suggests	suggest	VERB
fcis-31893	94	3	that	that	SCONJ
fcis-31893	94	4	although	although	SCONJ
fcis-31893	94	5	the	the	DET
fcis-31893	94	6	model	model	NOUN
fcis-31893	94	7	performs	perform	VERB
fcis-31893	94	8	well	well	ADV
fcis-31893	94	9	on	on	ADP
fcis-31893	94	10	the	the	DET
fcis-31893	94	11	training	training	NOUN
fcis-31893	94	12	data	datum	NOUN
fcis-31893	94	13	,	,	PUNCT
fcis-31893	94	14	there	there	PRON
fcis-31893	94	15	is	be	VERB
fcis-31893	94	16	some	some	DET
fcis-31893	94	17	generalization	generalization	NOUN
fcis-31893	94	18	error	error	NOUN
fcis-31893	94	19	on	on	ADP
fcis-31893	94	20	the	the	DET
fcis-31893	94	21	validation	validation	NOUN
fcis-31893	94	22	data	datum	NOUN
fcis-31893	94	23	.	.	PUNCT
fcis-31893	95	1	the	the	DET
fcis-31893	95	2	specific	specific	ADJ
fcis-31893	95	3	changes	change	NOUN
fcis-31893	95	4	in	in	ADP
fcis-31893	95	5	loss	loss	NOUN
fcis-31893	95	6	values	value	NOUN
fcis-31893	95	7	are	be	AUX
fcis-31893	95	8	as	as	SCONJ
fcis-31893	95	9	follows	follow	VERB
fcis-31893	95	10	:	:	PUNCT
fcis-31893	95	11	the	the	DET
fcis-31893	95	12	training	training	NOUN
fcis-31893	95	13	loss	loss	NOUN
fcis-31893	95	14	gradually	gradually	ADV
fcis-31893	95	15	decreases	decrease	VERB
fcis-31893	95	16	as	as	ADP
fcis-31893	95	17	training	training	NOUN
fcis-31893	95	18	progresses	progress	NOUN
fcis-31893	95	19	and	and	CCONJ
fcis-31893	95	20	eventually	eventually	ADV
fcis-31893	95	21	stabilizes	stabilize	VERB
fcis-31893	95	22	.	.	PUNCT
fcis-31893	96	1	the	the	DET
fcis-31893	96	2	evaluation	evaluation	NOUN
fcis-31893	96	3	loss	loss	NOUN
fcis-31893	96	4	fluctuates	fluctuate	NOUN
fcis-31893	96	5	compared	compare	VERB
fcis-31893	96	6	to	to	ADP
fcis-31893	96	7	the	the	DET
fcis-31893	96	8	training	training	NOUN
fcis-31893	96	9	loss	loss	NOUN
fcis-31893	96	10	but	but	CCONJ
fcis-31893	96	11	has	have	VERB
fcis-31893	96	12	an	an	DET
fcis-31893	96	13	overall	overall	ADJ
fcis-31893	96	14	declining	decline	VERB
fcis-31893	96	15	trend	trend	NOUN
fcis-31893	96	16	,	,	PUNCT
fcis-31893	96	17	confirming	confirm	VERB
fcis-31893	96	18	the	the	DET
fcis-31893	96	19	model	model	NOUN
fcis-31893	96	20	's	's	PART
fcis-31893	96	21	learning	learning	NOUN
fcis-31893	96	22	capability	capability	NOUN
fcis-31893	96	23	and	and	CCONJ
fcis-31893	96	24	effectiveness	effectiveness	NOUN
fcis-31893	96	25	.	.	PUNCT
fcis-31893	97	1	regarding	regard	VERB
fcis-31893	97	2	accuracy	accuracy	NOUN
fcis-31893	97	3	,	,	PUNCT
fcis-31893	97	4	the	the	DET
fcis-31893	97	5	comparison	comparison	NOUN
fcis-31893	97	6	between	between	ADP
fcis-31893	97	7	training	training	NOUN
fcis-31893	97	8	accuracy	accuracy	NOUN
fcis-31893	97	9	and	and	CCONJ
fcis-31893	97	10	evaluation	evaluation	NOUN
fcis-31893	97	11	accuracy	accuracy	NOUN
fcis-31893	97	12	reveals	reveal	VERB
fcis-31893	97	13	the	the	DET
fcis-31893	97	14	model	model	NOUN
fcis-31893	97	15	's	's	PART
fcis-31893	97	16	learning	learn	VERB
fcis-31893	97	17	status	status	NOUN
fcis-31893	97	18	.	.	PUNCT
fcis-31893	98	1	the	the	DET
fcis-31893	98	2	training	training	NOUN
fcis-31893	98	3	accuracy	accuracy	NOUN
fcis-31893	98	4	continues	continue	VERB
fcis-31893	98	5	to	to	PART
fcis-31893	98	6	rise	rise	VERB
fcis-31893	98	7	and	and	CCONJ
fcis-31893	98	8	approaches	approach	VERB
fcis-31893	98	9	100	100	NUM
fcis-31893	98	10	%	%	NOUN
fcis-31893	98	11	in	in	ADP
fcis-31893	98	12	the	the	DET
fcis-31893	98	13	final	final	ADJ
fcis-31893	98	14	stages	stage	NOUN
fcis-31893	98	15	,	,	PUNCT
fcis-31893	98	16	indicating	indicate	VERB
fcis-31893	98	17	that	that	SCONJ
fcis-31893	98	18	the	the	DET
fcis-31893	98	19	model	model	NOUN
fcis-31893	98	20	can	can	AUX
fcis-31893	98	21	effectively	effectively	ADV
fcis-31893	98	22	fit	fit	VERB
fcis-31893	98	23	the	the	DET
fcis-31893	98	24	training	training	NOUN
fcis-31893	98	25	data	datum	NOUN
fcis-31893	98	26	.	.	PUNCT
fcis-31893	99	1	however	however	ADV
fcis-31893	99	2	,	,	PUNCT
fcis-31893	99	3	the	the	DET
fcis-31893	99	4	evaluation	evaluation	NOUN
fcis-31893	99	5	accuracy	accuracy	NOUN
fcis-31893	99	6	is	be	AUX
fcis-31893	99	7	slightly	slightly	ADV
fcis-31893	99	8	lower	low	ADJ
fcis-31893	99	9	than	than	ADP
fcis-31893	99	10	the	the	DET
fcis-31893	99	11	training	training	NOUN
fcis-31893	99	12	accuracy	accuracy	NOUN
fcis-31893	99	13	,	,	PUNCT
fcis-31893	99	14	which	which	PRON
fcis-31893	99	15	indicates	indicate	VERB
fcis-31893	99	16	that	that	SCONJ
fcis-31893	99	17	the	the	DET
fcis-31893	99	18	model	model	NOUN
fcis-31893	99	19	's	's	PART
fcis-31893	99	20	performance	performance	NOUN
fcis-31893	99	21	on	on	ADP
fcis-31893	99	22	the	the	DET
fcis-31893	99	23	validation	validation	NOUN
fcis-31893	99	24	set	set	NOUN
fcis-31893	99	25	does	do	AUX
fcis-31893	99	26	not	not	PART
fcis-31893	99	27	fully	fully	ADV
fcis-31893	99	28	match	match	VERB
fcis-31893	99	29	that	that	SCONJ
fcis-31893	99	30	on	on	ADP
fcis-31893	99	31	the	the	DET
fcis-31893	99	32	training	training	NOUN
fcis-31893	99	33	set	set	NOUN
fcis-31893	99	34	.	.	PUNCT
fcis-31893	100	1	the	the	DET
fcis-31893	100	2	specific	specific	ADJ
fcis-31893	100	3	accuracy	accuracy	NOUN
fcis-31893	100	4	data	datum	NOUN
fcis-31893	100	5	is	be	AUX
fcis-31893	100	6	as	as	SCONJ
fcis-31893	100	7	follows	follow	VERB
fcis-31893	100	8	:	:	PUNCT
fcis-31893	100	9	training	training	NOUN
fcis-31893	100	10	accuracy	accuracy	NOUN
fcis-31893	100	11	steadily	steadily	ADV
fcis-31893	100	12	increases	increase	VERB
fcis-31893	100	13	as	as	ADP
fcis-31893	100	14	training	training	NOUN
fcis-31893	100	15	progresses	progress	NOUN
fcis-31893	100	16	,	,	PUNCT
fcis-31893	100	17	ultimately	ultimately	ADV
fcis-31893	100	18	approaching	approach	VERB
fcis-31893	100	19	100	100	NUM
fcis-31893	100	20	%	%	NOUN
fcis-31893	100	21	,	,	PUNCT
fcis-31893	100	22	while	while	SCONJ
fcis-31893	100	23	the	the	DET
fcis-31893	100	24	evaluation	evaluation	NOUN
fcis-31893	100	25	accuracy	accuracy	NOUN
fcis-31893	100	26	,	,	PUNCT
fcis-31893	100	27	although	although	SCONJ
fcis-31893	100	28	slightly	slightly	ADV
fcis-31893	100	29	lower	low	ADJ
fcis-31893	100	30	than	than	ADP
fcis-31893	100	31	the	the	DET
fcis-31893	100	32	training	training	NOUN
fcis-31893	100	33	accuracy	accuracy	NOUN
fcis-31893	100	34	,	,	PUNCT
fcis-31893	100	35	shows	show	VERB
fcis-31893	100	36	minimal	minimal	ADJ
fcis-31893	100	37	fluctuation	fluctuation	NOUN
fcis-31893	100	38	,	,	PUNCT
fcis-31893	100	39	indicating	indicate	VERB
fcis-31893	100	40	that	that	SCONJ
fcis-31893	100	41	the	the	DET
fcis-31893	100	42	model	model	NOUN
fcis-31893	100	43	possesses	possess	VERB
fcis-31893	100	44	a	a	DET
fcis-31893	100	45	certain	certain	ADJ
fcis-31893	100	46	degree	degree	NOUN
fcis-31893	100	47	of	of	ADP
fcis-31893	100	48	generalization	generalization	NOUN
fcis-31893	100	49	ability	ability	NOUN
fcis-31893	100	50	.	.	PUNCT
fcis-31893	101	1	in	in	ADP
fcis-31893	101	2	order	order	NOUN
fcis-31893	101	3	to	to	PART
fcis-31893	101	4	further	far	ADV
fcis-31893	101	5	analyze	analyze	VERB
fcis-31893	101	6	the	the	DET
fcis-31893	101	7	model	model	NOUN
fcis-31893	101	8	's	's	PART
fcis-31893	101	9	performance	performance	NOUN
fcis-31893	101	10	across	across	ADP
fcis-31893	101	11	different	different	ADJ
fcis-31893	101	12	categories	category	NOUN
fcis-31893	101	13	,	,	PUNCT
fcis-31893	101	14	we	we	PRON
fcis-31893	101	15	present	present	VERB
fcis-31893	101	16	the	the	DET
fcis-31893	101	17	confusion	confusion	NOUN
fcis-31893	101	18	matrix	matrix	NOUN
fcis-31893	101	19	of	of	ADP
fcis-31893	101	20	the	the	DET
fcis-31893	101	21	model	model	NOUN
fcis-31893	101	22	evaluation	evaluation	NOUN
fcis-31893	101	23	as	as	SCONJ
fcis-31893	101	24	shown	show	VERB
fcis-31893	101	25	in	in	ADP
fcis-31893	101	26	figure	figure	NOUN
fcis-31893	101	27	10	10	NUM
fcis-31893	101	28	.	.	PUNCT
fcis-31893	102	1	the	the	DET
fcis-31893	102	2	confusion	confusion	NOUN
fcis-31893	102	3	matrix	matrix	NOUN
fcis-31893	102	4	provides	provide	VERB
fcis-31893	102	5	a	a	DET
fcis-31893	102	6	comparison	comparison	NOUN
fcis-31893	102	7	between	between	ADP
fcis-31893	102	8	the	the	DET
fcis-31893	102	9	true	true	ADJ
fcis-31893	102	10	labels	label	NOUN
fcis-31893	102	11	and	and	CCONJ
fcis-31893	102	12	predicted	predict	VERB
fcis-31893	102	13	labels	label	NOUN
fcis-31893	102	14	for	for	ADP
fcis-31893	102	15	each	each	DET
fcis-31893	102	16	category	category	NOUN
fcis-31893	102	17	,	,	PUNCT
fcis-31893	102	18	which	which	PRON
fcis-31893	102	19	can	can	AUX
fcis-31893	102	20	help	help	VERB
fcis-31893	102	21	us	we	PRON
fcis-31893	102	22	identify	identify	VERB
fcis-31893	102	23	the	the	DET
fcis-31893	102	24	model	model	NOUN
fcis-31893	102	25	's	's	PART
fcis-31893	102	26	recognition	recognition	NOUN
fcis-31893	102	27	capability	capability	NOUN
fcis-31893	102	28	in	in	ADP
fcis-31893	102	29	specific	specific	ADJ
fcis-31893	102	30	categories	category	NOUN
fcis-31893	102	31	,	,	PUNCT
fcis-31893	102	32	especially	especially	ADV
fcis-31893	102	33	in	in	ADP
fcis-31893	102	34	terms	term	NOUN
fcis-31893	102	35	of	of	ADP
fcis-31893	102	36	misclassification	misclassification	NOUN
fcis-31893	102	37	.	.	PUNCT
fcis-31893	103	1	based	base	VERB
fcis-31893	103	2	on	on	ADP
fcis-31893	103	3	the	the	DET
fcis-31893	103	4	analysis	analysis	NOUN
fcis-31893	103	5	results	result	NOUN
fcis-31893	103	6	of	of	ADP
fcis-31893	103	7	the	the	DET
fcis-31893	103	8	confusion	confusion	NOUN
fcis-31893	103	9	matrix	matrix	NOUN
fcis-31893	103	10	,	,	PUNCT
fcis-31893	103	11	the	the	DET
fcis-31893	103	12	model	model	NOUN
fcis-31893	103	13	performs	perform	VERB
fcis-31893	103	14	well	well	ADV
fcis-31893	103	15	in	in	ADP
fcis-31893	103	16	most	most	ADJ
fcis-31893	103	17	categories	category	NOUN
fcis-31893	103	18	,	,	PUNCT
fcis-31893	103	19	but	but	CCONJ
fcis-31893	103	20	there	there	PRON
fcis-31893	103	21	are	be	VERB
fcis-31893	103	22	some	some	DET
fcis-31893	103	23	misclassification	misclassification	NOUN
fcis-31893	103	24	phenomena	phenomenon	NOUN
fcis-31893	103	25	in	in	ADP
fcis-31893	103	26	certain	certain	ADJ
fcis-31893	103	27	categories	category	NOUN
fcis-31893	103	28	,	,	PUNCT
fcis-31893	103	29	mainly	mainly	ADV
fcis-31893	103	30	manifested	manifest	VERB
fcis-31893	103	31	as	as	ADP
fcis-31893	103	32	confusion	confusion	NOUN
fcis-31893	103	33	between	between	ADP
fcis-31893	103	34	categories	category	NOUN
fcis-31893	103	35	.	.	PUNCT
fcis-31893	104	1	through	through	ADP
fcis-31893	104	2	the	the	DET
fcis-31893	104	3	analysis	analysis	NOUN
fcis-31893	104	4	of	of	ADP
fcis-31893	104	5	the	the	DET
fcis-31893	104	6	confusion	confusion	NOUN
fcis-31893	104	7	matrix	matrix	NOUN
fcis-31893	104	8	,	,	PUNCT
fcis-31893	104	9	the	the	DET
fcis-31893	104	10	model	model	NOUN
fcis-31893	104	11	demonstrates	demonstrate	VERB
fcis-31893	104	12	excellent	excellent	ADJ
fcis-31893	104	13	classification	classification	NOUN
fcis-31893	104	14	ability	ability	NOUN
fcis-31893	104	15	for	for	ADP
fcis-31893	104	16	most	most	ADJ
fcis-31893	104	17	categories	category	NOUN
fcis-31893	104	18	,	,	PUNCT
fcis-31893	104	19	with	with	ADP
fcis-31893	104	20	an	an	DET
fcis-31893	104	21	accuracy	accuracy	NOUN
fcis-31893	104	22	43	43	NUM
fcis-31893	104	23	rate	rate	NOUN
fcis-31893	104	24	exceeding	exceed	VERB
fcis-31893	104	25	98	98	NUM
fcis-31893	104	26	%	%	NOUN
fcis-31893	104	27	.	.	PUNCT
fcis-31893	105	1	for	for	ADP
fcis-31893	105	2	instance	instance	NOUN
fcis-31893	105	3	,	,	PUNCT
fcis-31893	105	4	the	the	DET
fcis-31893	105	5	classification	classification	NOUN
fcis-31893	105	6	accuracy	accuracy	NOUN
fcis-31893	105	7	for	for	ADP
fcis-31893	105	8	the	the	DET
fcis-31893	105	9	gun_shot	gun_shot	PROPN
fcis-31893	105	10	and	and	CCONJ
fcis-31893	105	11	jackhammer	jackhammer	PROPN
fcis-31893	105	12	categories	category	NOUN
fcis-31893	105	13	reached	reach	VERB
fcis-31893	105	14	100	100	NUM
fcis-31893	105	15	%	%	NOUN
fcis-31893	105	16	,	,	PUNCT
fcis-31893	105	17	indicating	indicate	VERB
fcis-31893	105	18	that	that	SCONJ
fcis-31893	105	19	the	the	DET
fcis-31893	105	20	model	model	NOUN
fcis-31893	105	21	shows	show	VERB
fcis-31893	105	22	extremely	extremely	ADV
fcis-31893	105	23	high	high	ADJ
fcis-31893	105	24	sensitivity	sensitivity	NOUN
fcis-31893	105	25	to	to	PART
fcis-31893	105	26	sounds	sound	VERB
fcis-31893	105	27	with	with	ADP
fcis-31893	105	28	significantly	significantly	ADV
fcis-31893	105	29	high	high	ADJ
fcis-31893	105	30	-	-	PUNCT
fcis-31893	105	31	frequency	frequency	NOUN
fcis-31893	105	32	features	feature	NOUN
fcis-31893	105	33	.	.	PUNCT
fcis-31893	106	1	fig	fig	NOUN
fcis-31893	106	2	8	8	NUM
fcis-31893	106	3	.	.	PUNCT
fcis-31893	107	1	comparison	comparison	NOUN
fcis-31893	107	2	of	of	ADP
fcis-31893	107	3	training	training	NOUN
fcis-31893	107	4	loss	loss	NOUN
fcis-31893	107	5	and	and	CCONJ
fcis-31893	107	6	evaluation	evaluation	NOUN
fcis-31893	107	7	loss	loss	NOUN
fcis-31893	107	8	fig	fig	NOUN
fcis-31893	107	9	9	9	NUM
fcis-31893	107	10	.	.	PUNCT
fcis-31893	107	11	comparison	comparison	NOUN
fcis-31893	107	12	of	of	ADP
fcis-31893	107	13	training	training	NOUN
fcis-31893	107	14	accuracy	accuracy	NOUN
fcis-31893	107	15	and	and	CCONJ
fcis-31893	107	16	evaluation	evaluation	NOUN
fcis-31893	107	17	accuracy	accuracy	NOUN
fcis-31893	107	18	fig	fig	NOUN
fcis-31893	107	19	10	10	NUM
fcis-31893	107	20	.	.	PUNCT
fcis-31893	108	1	confusion	confusion	NOUN
fcis-31893	108	2	matrix	matrix	NOUN
fcis-31893	108	3	of	of	ADP
fcis-31893	108	4	the	the	DET
fcis-31893	108	5	experimental	experimental	ADJ
fcis-31893	108	6	model	model	NOUN
fcis-31893	108	7	on	on	ADP
fcis-31893	108	8	the	the	DET
fcis-31893	108	9	test	test	NOUN
fcis-31893	108	10	set	set	VERB
fcis-31893	108	11	44	44	NUM
fcis-31893	108	12	3.3	3.3	NUM
fcis-31893	108	13	.	.	PUNCT
fcis-31893	109	1	experimental	experimental	ADJ
fcis-31893	109	2	comparative	comparative	ADJ
fcis-31893	109	3	analysis	analysis	NOUN
fcis-31893	109	4	in	in	ADP
fcis-31893	109	5	order	order	NOUN
fcis-31893	109	6	to	to	PART
fcis-31893	109	7	comprehensively	comprehensively	ADV
fcis-31893	109	8	evaluate	evaluate	VERB
fcis-31893	109	9	the	the	DET
fcis-31893	109	10	performance	performance	NOUN
fcis-31893	109	11	of	of	ADP
fcis-31893	109	12	the	the	DET
fcis-31893	109	13	convnext	convnext	ADJ
fcis-31893	109	14	-	-	PUNCT
fcis-31893	109	15	feca	feca	NOUN
fcis-31893	109	16	model	model	NOUN
fcis-31893	109	17	proposed	propose	VERB
fcis-31893	109	18	in	in	ADP
fcis-31893	109	19	this	this	DET
fcis-31893	109	20	paper	paper	NOUN
fcis-31893	109	21	for	for	ADP
fcis-31893	109	22	audio	audio	ADJ
fcis-31893	109	23	classification	classification	NOUN
fcis-31893	109	24	tasks	task	NOUN
fcis-31893	109	25	,	,	PUNCT
fcis-31893	109	26	we	we	PRON
fcis-31893	109	27	conducted	conduct	VERB
fcis-31893	109	28	comparative	comparative	ADJ
fcis-31893	109	29	experiments	experiment	NOUN
fcis-31893	109	30	with	with	ADP
fcis-31893	109	31	several	several	ADJ
fcis-31893	109	32	current	current	ADJ
fcis-31893	109	33	mainstream	mainstream	NOUN
fcis-31893	109	34	audio	audio	NOUN
fcis-31893	109	35	analysis	analysis	NOUN
fcis-31893	109	36	models	model	NOUN
fcis-31893	109	37	,	,	PUNCT
fcis-31893	109	38	including	include	VERB
fcis-31893	109	39	eres2netv2[8	eres2netv2[8	PROPN
fcis-31893	109	40	]	]	PUNCT
fcis-31893	109	41	,	,	PUNCT
fcis-31893	109	42	resnetse	resnetse	NOUN
fcis-31893	109	43	,	,	PUNCT
fcis-31893	109	44	eres2net	eres2net	NOUN
fcis-31893	109	45	,	,	PUNCT
fcis-31893	109	46	campplus	campplus	NOUN
fcis-31893	110	1	[	[	X
fcis-31893	110	2	9	9	NUM
fcis-31893	110	3	]	]	PUNCT
fcis-31893	110	4	,	,	PUNCT
fcis-31893	110	5	panns	pann	NOUN
fcis-31893	110	6	(	(	PUNCT
fcis-31893	110	7	cnn10)[10	cnn10)[10	VERB
fcis-31893	110	8	]	]	PUNCT
fcis-31893	110	9	,	,	PUNCT
fcis-31893	110	10	and	and	CCONJ
fcis-31893	110	11	ecapatdnn[11	ecapatdnn[11	PROPN
fcis-31893	110	12	]	]	PUNCT
fcis-31893	110	13	.	.	PUNCT
fcis-31893	111	1	the	the	DET
fcis-31893	111	2	experiments	experiment	NOUN
fcis-31893	111	3	were	be	AUX
fcis-31893	111	4	uniformly	uniformly	ADV
fcis-31893	111	5	based	base	VERB
fcis-31893	111	6	on	on	ADP
fcis-31893	111	7	the	the	DET
fcis-31893	111	8	urbansound8k	urbansound8k	PROPN
fcis-31893	111	9	dataset	dataset	NOUN
fcis-31893	111	10	,	,	PUNCT
fcis-31893	111	11	using	use	VERB
fcis-31893	111	12	a	a	DET
fcis-31893	111	13	10	10	NUM
fcis-31893	111	14	-	-	PUNCT
fcis-31893	111	15	class	class	NOUN
fcis-31893	111	16	audio	audio	NOUN
fcis-31893	111	17	classification	classification	NOUN
fcis-31893	111	18	task	task	NOUN
fcis-31893	111	19	as	as	ADP
fcis-31893	111	20	the	the	DET
fcis-31893	111	21	experimental	experimental	ADJ
fcis-31893	111	22	object	object	NOUN
fcis-31893	111	23	,	,	PUNCT
fcis-31893	111	24	and	and	CCONJ
fcis-31893	111	25	consistently	consistently	ADV
fcis-31893	111	26	employing	employ	VERB
fcis-31893	111	27	spectrogram	spectrogram	NOUN
fcis-31893	111	28	as	as	ADP
fcis-31893	111	29	the	the	DET
fcis-31893	111	30	feature	feature	NOUN
fcis-31893	111	31	extraction	extraction	NOUN
fcis-31893	111	32	method	method	NOUN
fcis-31893	111	33	.	.	PUNCT
fcis-31893	112	1	table	table	NOUN
fcis-31893	112	2	1	1	NUM
fcis-31893	112	3	shows	show	VERB
fcis-31893	112	4	the	the	DET
fcis-31893	112	5	classification	classification	NOUN
fcis-31893	112	6	accuracy	accuracy	NOUN
fcis-31893	112	7	of	of	ADP
fcis-31893	112	8	different	different	ADJ
fcis-31893	112	9	models	model	NOUN
fcis-31893	112	10	,	,	PUNCT
fcis-31893	112	11	where	where	SCONJ
fcis-31893	112	12	the	the	DET
fcis-31893	112	13	accuracy	accuracy	NOUN
fcis-31893	112	14	of	of	ADP
fcis-31893	112	15	the	the	DET
fcis-31893	112	16	convnext	convnext	ADJ
fcis-31893	112	17	-	-	PUNCT
fcis-31893	112	18	feca	feca	NOUN
fcis-31893	112	19	model	model	NOUN
fcis-31893	112	20	reaches	reach	VERB
fcis-31893	112	21	0.978	0.978	NUM
fcis-31893	112	22	,	,	PUNCT
fcis-31893	112	23	outperforming	outperform	VERB
fcis-31893	112	24	all	all	DET
fcis-31893	112	25	comparison	comparison	NOUN
fcis-31893	112	26	models	model	NOUN
fcis-31893	112	27	.	.	PUNCT
fcis-31893	113	1	compared	compare	VERB
fcis-31893	113	2	to	to	ADP
fcis-31893	113	3	the	the	DET
fcis-31893	113	4	standard	standard	ADJ
fcis-31893	113	5	convnext	convnext	NOUN
fcis-31893	113	6	model	model	NOUN
fcis-31893	113	7	(	(	PUNCT
fcis-31893	113	8	accuracy	accuracy	NOUN
fcis-31893	113	9	0.976	0.976	NUM
fcis-31893	113	10	)	)	PUNCT
fcis-31893	113	11	,	,	PUNCT
fcis-31893	113	12	the	the	DET
fcis-31893	113	13	feca	feca	PROPN
fcis-31893	113	14	mechanism	mechanism	NOUN
fcis-31893	113	15	enhances	enhance	VERB
fcis-31893	113	16	the	the	DET
fcis-31893	113	17	ability	ability	NOUN
fcis-31893	113	18	to	to	PART
fcis-31893	113	19	extract	extract	VERB
fcis-31893	113	20	frequency	frequency	NOUN
fcis-31893	113	21	domain	domain	NOUN
fcis-31893	113	22	features	feature	NOUN
fcis-31893	113	23	and	and	CCONJ
fcis-31893	113	24	combines	combine	VERB
fcis-31893	113	25	multi	multi	ADJ
fcis-31893	113	26	-	-	ADJ
fcis-31893	113	27	scale	scale	ADJ
fcis-31893	113	28	convolution	convolution	NOUN
fcis-31893	113	29	operations	operation	NOUN
fcis-31893	113	30	,	,	PUNCT
fcis-31893	113	31	giving	give	VERB
fcis-31893	113	32	the	the	DET
fcis-31893	113	33	model	model	NOUN
fcis-31893	113	34	an	an	DET
fcis-31893	113	35	advantage	advantage	NOUN
fcis-31893	113	36	in	in	ADP
fcis-31893	113	37	feature	feature	NOUN
fcis-31893	113	38	representation	representation	NOUN
fcis-31893	113	39	for	for	ADP
fcis-31893	113	40	complex	complex	ADJ
fcis-31893	113	41	environmental	environmental	ADJ
fcis-31893	113	42	audio	audio	NOUN
fcis-31893	113	43	.	.	PUNCT
fcis-31893	114	1	moreover	moreover	ADV
fcis-31893	114	2	,	,	PUNCT
fcis-31893	114	3	compared	compare	VERB
fcis-31893	114	4	with	with	ADP
fcis-31893	114	5	classic	classic	ADJ
fcis-31893	114	6	models	model	NOUN
fcis-31893	114	7	such	such	ADJ
fcis-31893	114	8	as	as	ADP
fcis-31893	114	9	resnetse	resnetse	NOUN
fcis-31893	114	10	and	and	CCONJ
fcis-31893	114	11	campplus	campplus	ADJ
fcis-31893	114	12	,	,	PUNCT
fcis-31893	114	13	the	the	DET
fcis-31893	114	14	classification	classification	NOUN
fcis-31893	114	15	performance	performance	NOUN
fcis-31893	114	16	of	of	ADP
fcis-31893	114	17	convnext	convnext	NOUN
fcis-31893	114	18	-	-	PUNCT
fcis-31893	114	19	feca	feca	NOUN
fcis-31893	114	20	is	be	AUX
fcis-31893	114	21	significantly	significantly	ADV
fcis-31893	114	22	improved	improve	VERB
fcis-31893	114	23	,	,	PUNCT
fcis-31893	114	24	further	far	ADV
fcis-31893	114	25	proving	prove	VERB
fcis-31893	114	26	its	its	PRON
fcis-31893	114	27	superiority	superiority	NOUN
fcis-31893	114	28	on	on	ADP
fcis-31893	114	29	the	the	DET
fcis-31893	114	30	urbansound8k	urbansound8k	NOUN
fcis-31893	114	31	dataset	dataset	NOUN
fcis-31893	114	32	.	.	PUNCT
fcis-31893	115	1	these	these	DET
fcis-31893	115	2	experimental	experimental	ADJ
fcis-31893	115	3	results	result	NOUN
fcis-31893	115	4	indicate	indicate	VERB
fcis-31893	115	5	that	that	SCONJ
fcis-31893	115	6	the	the	DET
fcis-31893	115	7	convnext	convnext	PROPN
fcis-31893	115	8	-	-	PUNCT
fcis-31893	115	9	feca	feca	NOUN
fcis-31893	115	10	model	model	NOUN
fcis-31893	115	11	proposed	propose	VERB
fcis-31893	115	12	in	in	ADP
fcis-31893	115	13	this	this	DET
fcis-31893	115	14	paper	paper	NOUN
fcis-31893	115	15	has	have	VERB
fcis-31893	115	16	strong	strong	ADJ
fcis-31893	115	17	generalization	generalization	NOUN
fcis-31893	115	18	ability	ability	NOUN
fcis-31893	115	19	and	and	CCONJ
fcis-31893	115	20	robustness	robustness	NOUN
fcis-31893	115	21	in	in	ADP
fcis-31893	115	22	audio	audio	ADJ
fcis-31893	115	23	classification	classification	NOUN
fcis-31893	115	24	tasks	task	NOUN
fcis-31893	115	25	.	.	PUNCT
fcis-31893	116	1	table	table	NOUN
fcis-31893	116	2	1	1	NUM
fcis-31893	116	3	.	.	PUNCT
fcis-31893	116	4	performance	performance	NOUN
fcis-31893	116	5	indicators	indicator	NOUN
fcis-31893	116	6	of	of	ADP
fcis-31893	116	7	mainstream	mainstream	ADJ
fcis-31893	116	8	audio	audio	NOUN
fcis-31893	116	9	analysis	analysis	NOUN
fcis-31893	116	10	models	model	NOUN
fcis-31893	116	11	model	model	NOUN
fcis-31893	116	12	preprocessing	preprocessing	NOUN
fcis-31893	116	13	method	method	NOUN
fcis-31893	116	14	dataset	dataset	NOUN
fcis-31893	116	15	number	number	NOUN
fcis-31893	116	16	of	of	ADP
fcis-31893	116	17	categories	category	NOUN
fcis-31893	116	18	accuracy	accuracy	PROPN
fcis-31893	116	19	convnextfeca	convnextfeca	NOUN
fcis-31893	116	20	spectrogram	spectrogram	NOUN
fcis-31893	116	21	urbansound8k	urbansound8k	PROPN
fcis-31893	116	22	10	10	NUM
fcis-31893	116	23	0.978	0.978	NUM
fcis-31893	116	24	convnext	convnext	ADJ
fcis-31893	116	25	spectrogram	spectrogram	NOUN
fcis-31893	116	26	urbansound8k	urbansound8k	NOUN
fcis-31893	116	27	10	10	NUM
fcis-31893	116	28	0.969	0.969	NUM
fcis-31893	116	29	eres2netv2	eres2netv2	PROPN
fcis-31893	116	30	spectrogram	spectrogram	NOUN
fcis-31893	116	31	urbansound8k	urbansound8k	NOUN
fcis-31893	116	32	10	10	NUM
fcis-31893	116	33	0.956	0.956	NUM
fcis-31893	116	34	resnetse	resnetse	NOUN
fcis-31893	116	35	spectrogram	spectrogram	NOUN
fcis-31893	116	36	urbansound8k	urbansound8k	NOUN
fcis-31893	116	37	10	10	NUM
fcis-31893	116	38	0.955	0.955	NUM
fcis-31893	116	39	eres2net	eres2net	NOUN
fcis-31893	116	40	spectrogram	spectrogram	NOUN
fcis-31893	116	41	urbansound8k	urbansound8k	NOUN
fcis-31893	116	42	10	10	NUM
fcis-31893	116	43	0.948	0.948	NUM
fcis-31893	116	44	campplus	campplus	NOUN
fcis-31893	116	45	spectrogram	spectrogram	NOUN
fcis-31893	116	46	urbansound8k	urbansound8k	NOUN
fcis-31893	116	47	10	10	NUM
fcis-31893	116	48	0.954	0.954	NUM
fcis-31893	116	49	panns	pann	NOUN
fcis-31893	116	50	(	(	PUNCT
fcis-31893	116	51	cnn10	cnn10	NOUN
fcis-31893	116	52	)	)	PUNCT
fcis-31893	116	53	spectrogram	spectrogram	NOUN
fcis-31893	116	54	urbansound8k	urbansound8k	NOUN
fcis-31893	116	55	10	10	NUM
fcis-31893	116	56	0.938	0.938	NUM
fcis-31893	116	57	ecapatdnn	ecapatdnn	PROPN
fcis-31893	116	58	spectrogram	spectrogram	NOUN
fcis-31893	116	59	urbansound8k	urbansound8k	NOUN
fcis-31893	116	60	10	10	NUM
fcis-31893	116	61	0.935	0.935	NUM
fcis-31893	116	62	in	in	ADP
fcis-31893	116	63	order	order	NOUN
fcis-31893	116	64	to	to	PART
fcis-31893	116	65	further	far	ADV
fcis-31893	116	66	explore	explore	VERB
fcis-31893	116	67	the	the	DET
fcis-31893	116	68	impact	impact	NOUN
fcis-31893	116	69	of	of	ADP
fcis-31893	116	70	different	different	ADJ
fcis-31893	116	71	feature	feature	NOUN
fcis-31893	116	72	extraction	extraction	NOUN
fcis-31893	116	73	methods	method	NOUN
fcis-31893	116	74	on	on	ADP
fcis-31893	116	75	model	model	NOUN
fcis-31893	116	76	performance	performance	NOUN
fcis-31893	116	77	,	,	PUNCT
fcis-31893	116	78	we	we	PRON
fcis-31893	116	79	based	base	VERB
fcis-31893	116	80	our	our	PRON
fcis-31893	116	81	experiments	experiment	NOUN
fcis-31893	116	82	on	on	ADP
fcis-31893	116	83	the	the	DET
fcis-31893	116	84	convnext	convnext	ADJ
fcis-31893	116	85	-	-	PUNCT
fcis-31893	116	86	feca	feca	NOUN
fcis-31893	116	87	model	model	NOUN
fcis-31893	116	88	,	,	PUNCT
fcis-31893	116	89	using	use	VERB
fcis-31893	116	90	spectrogram	spectrogram	NOUN
fcis-31893	116	91	,	,	PUNCT
fcis-31893	116	92	mfcc	mfcc	NOUN
fcis-31893	116	93	,	,	PUNCT
fcis-31893	116	94	melspectrogram	melspectrogram	NOUN
fcis-31893	116	95	,	,	PUNCT
fcis-31893	116	96	and	and	CCONJ
fcis-31893	116	97	various	various	ADJ
fcis-31893	116	98	feature	feature	NOUN
fcis-31893	116	99	fusion	fusion	NOUN
fcis-31893	116	100	methods	method	NOUN
fcis-31893	116	101	(	(	PUNCT
fcis-31893	116	102	such	such	ADJ
fcis-31893	116	103	as	as	ADP
fcis-31893	116	104	spectrogram	spectrogram	NOUN
fcis-31893	116	105	mfcc	mfcc	NOUN
fcis-31893	116	106	and	and	CCONJ
fcis-31893	116	107	mfcc	mfcc	NOUN
fcis-31893	116	108	melspectrogram	melspectrogram	NOUN
fcis-31893	116	109	,	,	PUNCT
fcis-31893	116	110	etc	etc	X
fcis-31893	116	111	.	.	X
fcis-31893	116	112	)	)	PUNCT
fcis-31893	116	113	for	for	ADP
fcis-31893	116	114	experimental	experimental	ADJ
fcis-31893	116	115	analysis	analysis	NOUN
fcis-31893	116	116	.	.	PUNCT
fcis-31893	117	1	the	the	DET
fcis-31893	117	2	results	result	NOUN
fcis-31893	117	3	are	be	AUX
fcis-31893	117	4	shown	show	VERB
fcis-31893	117	5	in	in	ADP
fcis-31893	117	6	table	table	NOUN
fcis-31893	117	7	2	2	NUM
fcis-31893	117	8	.	.	PUNCT
fcis-31893	118	1	when	when	SCONJ
fcis-31893	118	2	using	use	VERB
fcis-31893	118	3	spectrogram	spectrogram	NOUN
fcis-31893	118	4	for	for	ADP
fcis-31893	118	5	feature	feature	NOUN
fcis-31893	118	6	extraction	extraction	NOUN
fcis-31893	118	7	,	,	PUNCT
fcis-31893	118	8	the	the	DET
fcis-31893	118	9	model	model	NOUN
fcis-31893	118	10	's	's	PART
fcis-31893	118	11	accuracy	accuracy	NOUN
fcis-31893	118	12	reached	reach	VERB
fcis-31893	118	13	0.978	0.978	NUM
fcis-31893	118	14	,	,	PUNCT
fcis-31893	118	15	while	while	SCONJ
fcis-31893	118	16	the	the	DET
fcis-31893	118	17	accuracies	accuracy	NOUN
fcis-31893	118	18	for	for	ADP
fcis-31893	118	19	mfcc	mfcc	NOUN
fcis-31893	118	20	and	and	CCONJ
fcis-31893	118	21	melspectrogram	melspectrogram	NOUN
fcis-31893	118	22	were	be	AUX
fcis-31893	118	23	0.976	0.976	NUM
fcis-31893	118	24	and	and	CCONJ
fcis-31893	118	25	0.964	0.964	NUM
fcis-31893	118	26	,	,	PUNCT
fcis-31893	118	27	respectively	respectively	ADV
fcis-31893	118	28	.	.	PUNCT
fcis-31893	119	1	this	this	PRON
fcis-31893	119	2	indicates	indicate	VERB
fcis-31893	119	3	that	that	SCONJ
fcis-31893	119	4	spectrogram	spectrogram	NOUN
fcis-31893	119	5	has	have	VERB
fcis-31893	119	6	certain	certain	ADJ
fcis-31893	119	7	advantages	advantage	NOUN
fcis-31893	119	8	in	in	ADP
fcis-31893	119	9	capturing	capture	VERB
fcis-31893	119	10	the	the	DET
fcis-31893	119	11	time	time	NOUN
fcis-31893	119	12	-	-	PUNCT
fcis-31893	119	13	frequency	frequency	NOUN
fcis-31893	119	14	domain	domain	NOUN
fcis-31893	119	15	information	information	NOUN
fcis-31893	119	16	of	of	ADP
fcis-31893	119	17	audio	audio	NOUN
fcis-31893	119	18	.	.	PUNCT
fcis-31893	120	1	however	however	ADV
fcis-31893	120	2	,	,	PUNCT
fcis-31893	120	3	when	when	SCONJ
fcis-31893	120	4	we	we	PRON
fcis-31893	120	5	fused	fuse	VERB
fcis-31893	120	6	different	different	ADJ
fcis-31893	120	7	feature	feature	NOUN
fcis-31893	120	8	extraction	extraction	NOUN
fcis-31893	120	9	methods	method	NOUN
fcis-31893	120	10	,	,	PUNCT
fcis-31893	120	11	the	the	DET
fcis-31893	120	12	model	model	NOUN
fcis-31893	120	13	's	's	PART
fcis-31893	120	14	classification	classification	NOUN
fcis-31893	120	15	performance	performance	NOUN
fcis-31893	120	16	was	be	AUX
fcis-31893	120	17	significantly	significantly	ADV
fcis-31893	120	18	improved	improve	VERB
fcis-31893	120	19	,	,	PUNCT
fcis-31893	120	20	especially	especially	ADV
fcis-31893	120	21	when	when	SCONJ
fcis-31893	120	22	using	use	VERB
fcis-31893	120	23	the	the	DET
fcis-31893	120	24	fused	fuse	VERB
fcis-31893	120	25	features	feature	NOUN
fcis-31893	120	26	of	of	ADP
fcis-31893	120	27	spectrogram	spectrogram	NOUN
fcis-31893	120	28	and	and	CCONJ
fcis-31893	120	29	mfcc	mfcc	NOUN
fcis-31893	120	30	,	,	PUNCT
fcis-31893	120	31	achieving	achieve	VERB
fcis-31893	120	32	a	a	DET
fcis-31893	120	33	classification	classification	NOUN
fcis-31893	120	34	accuracy	accuracy	NOUN
fcis-31893	120	35	of	of	ADP
fcis-31893	120	36	0.985	0.985	NUM
fcis-31893	120	37	.	.	PUNCT
fcis-31893	121	1	this	this	DET
fcis-31893	121	2	result	result	NOUN
fcis-31893	121	3	suggests	suggest	VERB
fcis-31893	121	4	that	that	SCONJ
fcis-31893	121	5	spectrogram	spectrogram	NOUN
fcis-31893	121	6	and	and	CCONJ
fcis-31893	121	7	mfcc	mfcc	NOUN
fcis-31893	121	8	have	have	VERB
fcis-31893	121	9	complementary	complementary	ADJ
fcis-31893	121	10	characteristics	characteristic	NOUN
fcis-31893	121	11	in	in	ADP
fcis-31893	121	12	terms	term	NOUN
fcis-31893	121	13	of	of	ADP
fcis-31893	121	14	time	time	NOUN
fcis-31893	121	15	-	-	PUNCT
fcis-31893	121	16	frequency	frequency	NOUN
fcis-31893	121	17	domain	domain	NOUN
fcis-31893	121	18	information	information	NOUN
fcis-31893	121	19	and	and	CCONJ
fcis-31893	121	20	their	their	PRON
fcis-31893	121	21	fused	fuse	VERB
fcis-31893	121	22	features	feature	NOUN
fcis-31893	121	23	can	can	AUX
fcis-31893	121	24	represent	represent	VERB
fcis-31893	121	25	the	the	DET
fcis-31893	121	26	complexity	complexity	NOUN
fcis-31893	121	27	of	of	ADP
fcis-31893	121	28	audio	audio	ADJ
fcis-31893	121	29	signals	signal	NOUN
fcis-31893	121	30	more	more	ADV
fcis-31893	121	31	comprehensively	comprehensively	ADV
fcis-31893	121	32	.	.	PUNCT
fcis-31893	122	1	additionally	additionally	ADV
fcis-31893	122	2	,	,	PUNCT
fcis-31893	122	3	the	the	DET
fcis-31893	122	4	accuracies	accuracy	NOUN
fcis-31893	122	5	when	when	SCONJ
fcis-31893	122	6	using	use	VERB
fcis-31893	122	7	the	the	DET
fcis-31893	122	8	features	feature	NOUN
fcis-31893	122	9	of	of	ADP
fcis-31893	122	10	spectrogram	spectrogram	NOUN
fcis-31893	122	11	melspectrogram	melspectrogram	NOUN
fcis-31893	122	12	and	and	CCONJ
fcis-31893	122	13	mfcc	mfcc	NOUN
fcis-31893	122	14	melspectrogram	melspectrogram	PROPN
fcis-31893	122	15	also	also	ADV
fcis-31893	122	16	reached	reach	VERB
fcis-31893	122	17	0.972	0.972	NUM
fcis-31893	122	18	and	and	CCONJ
fcis-31893	122	19	0.970	0.970	NUM
fcis-31893	122	20	,	,	PUNCT
fcis-31893	122	21	respectively	respectively	ADV
fcis-31893	122	22	,	,	PUNCT
fcis-31893	122	23	both	both	PRON
fcis-31893	122	24	of	of	ADP
fcis-31893	122	25	which	which	PRON
fcis-31893	122	26	outperformed	outperform	VERB
fcis-31893	122	27	the	the	DET
fcis-31893	122	28	single	single	ADJ
fcis-31893	122	29	feature	feature	NOUN
fcis-31893	122	30	extraction	extraction	NOUN
fcis-31893	122	31	method	method	NOUN
fcis-31893	122	32	.	.	PUNCT
fcis-31893	123	1	table	table	NOUN
fcis-31893	123	2	2	2	NUM
fcis-31893	123	3	.	.	PUNCT
fcis-31893	123	4	performance	performance	NOUN
fcis-31893	123	5	metrics	metric	NOUN
fcis-31893	123	6	of	of	ADP
fcis-31893	123	7	the	the	DET
fcis-31893	123	8	convnext	convnext	ADJ
fcis-31893	123	9	-	-	PUNCT
fcis-31893	123	10	feca	feca	NOUN
fcis-31893	123	11	model	model	NOUN
fcis-31893	123	12	with	with	ADP
fcis-31893	123	13	different	different	ADJ
fcis-31893	123	14	feature	feature	NOUN
fcis-31893	123	15	extraction	extraction	NOUN
fcis-31893	123	16	methods	method	NOUN
fcis-31893	123	17	.	.	PUNCT
fcis-31893	124	1	model	model	NOUN
fcis-31893	124	2	preprocessing	preprocessing	NOUN
fcis-31893	124	3	method	method	PROPN
fcis-31893	124	4	accuracy	accuracy	NOUN
fcis-31893	124	5	convnextfeca	convnextfeca	NOUN
fcis-31893	124	6	spectrogram	spectrogram	NOUN
fcis-31893	124	7	0.978	0.978	NUM
fcis-31893	124	8	convnextfeca	convnextfeca	NOUN
fcis-31893	124	9	mfcc	mfcc	NOUN
fcis-31893	124	10	0.976	0.976	NUM
fcis-31893	124	11	convnextfeca	convnextfeca	NOUN
fcis-31893	124	12	melspectrogram	melspectrogram	PROPN
fcis-31893	124	13	0.964	0.964	NUM
fcis-31893	124	14	convnextfeca	convnextfeca	NOUN
fcis-31893	124	15	spectrogram+mfcc	spectrogram+mfcc	PROPN
fcis-31893	124	16	0.985	0.985	NUM
fcis-31893	124	17	convnextfeca	convnextfeca	NOUN
fcis-31893	124	18	spectrogram+melspectrogram	spectrogram+melspectrogram	PROPN
fcis-31893	124	19	0.972	0.972	NUM
fcis-31893	124	20	convnextfeca	convnextfeca	NOUN
fcis-31893	124	21	mfcc+melspectrogram	mfcc+melspectrogram	PROPN
fcis-31893	124	22	0.970	0.970	NUM
fcis-31893	124	23	based	base	VERB
fcis-31893	124	24	on	on	ADP
fcis-31893	124	25	the	the	DET
fcis-31893	124	26	experimental	experimental	ADJ
fcis-31893	124	27	results	result	NOUN
fcis-31893	124	28	,	,	PUNCT
fcis-31893	124	29	it	it	PRON
fcis-31893	124	30	can	can	AUX
fcis-31893	124	31	be	be	AUX
fcis-31893	124	32	seen	see	VERB
fcis-31893	124	33	that	that	SCONJ
fcis-31893	124	34	the	the	DET
fcis-31893	124	35	convnext	convnext	PROPN
fcis-31893	124	36	-	-	PUNCT
fcis-31893	124	37	feca	feca	NOUN
fcis-31893	124	38	model	model	NOUN
fcis-31893	124	39	,	,	PUNCT
fcis-31893	124	40	by	by	ADP
fcis-31893	124	41	introducing	introduce	VERB
fcis-31893	124	42	the	the	DET
fcis-31893	124	43	frequency	frequency	NOUN
fcis-31893	124	44	domain	domain	NOUN
fcis-31893	124	45	enhanced	enhance	VERB
fcis-31893	124	46	eca	eca	NOUN
fcis-31893	124	47	attention	attention	NOUN
fcis-31893	124	48	mechanism	mechanism	NOUN
fcis-31893	124	49	,	,	PUNCT
fcis-31893	124	50	effectively	effectively	ADV
fcis-31893	124	51	improves	improve	VERB
fcis-31893	124	52	the	the	DET
fcis-31893	124	53	multi	multi	ADJ
fcis-31893	124	54	-	-	ADJ
fcis-31893	124	55	scale	scale	ADJ
fcis-31893	124	56	feature	feature	NOUN
fcis-31893	124	57	extraction	extraction	NOUN
fcis-31893	124	58	capability	capability	NOUN
fcis-31893	124	59	and	and	CCONJ
fcis-31893	124	60	has	have	VERB
fcis-31893	124	61	significant	significant	ADJ
fcis-31893	124	62	advantages	advantage	NOUN
fcis-31893	124	63	in	in	ADP
fcis-31893	124	64	the	the	DET
fcis-31893	124	65	complexity	complexity	NOUN
fcis-31893	124	66	modeling	modeling	NOUN
fcis-31893	124	67	of	of	ADP
fcis-31893	124	68	audio	audio	ADJ
fcis-31893	124	69	signals	signal	NOUN
fcis-31893	124	70	.	.	PUNCT
fcis-31893	125	1	meanwhile	meanwhile	ADV
fcis-31893	125	2	,	,	PUNCT
fcis-31893	125	3	the	the	DET
fcis-31893	125	4	choice	choice	NOUN
fcis-31893	125	5	of	of	ADP
fcis-31893	125	6	feature	feature	NOUN
fcis-31893	125	7	extraction	extraction	NOUN
fcis-31893	125	8	method	method	NOUN
fcis-31893	125	9	has	have	VERB
fcis-31893	125	10	a	a	DET
fcis-31893	125	11	critical	critical	ADJ
fcis-31893	125	12	impact	impact	NOUN
fcis-31893	125	13	on	on	ADP
fcis-31893	125	14	model	model	NOUN
fcis-31893	125	15	performance	performance	NOUN
fcis-31893	125	16	;	;	PUNCT
fcis-31893	125	17	among	among	ADP
fcis-31893	125	18	the	the	DET
fcis-31893	125	19	single	single	ADJ
fcis-31893	125	20	-	-	PUNCT
fcis-31893	125	21	feature	feature	NOUN
fcis-31893	125	22	methods	method	NOUN
fcis-31893	125	23	,	,	PUNCT
fcis-31893	125	24	spectrogram	spectrogram	NOUN
fcis-31893	125	25	performs	perform	VERB
fcis-31893	125	26	the	the	DET
fcis-31893	125	27	best	good	ADJ
fcis-31893	125	28	,	,	PUNCT
fcis-31893	125	29	while	while	SCONJ
fcis-31893	125	30	the	the	DET
fcis-31893	125	31	combination	combination	NOUN
fcis-31893	125	32	of	of	ADP
fcis-31893	125	33	fused	fuse	VERB
fcis-31893	125	34	features	feature	NOUN
fcis-31893	125	35	,	,	PUNCT
fcis-31893	125	36	especially	especially	ADV
fcis-31893	125	37	spectrogram	spectrogram	NOUN
fcis-31893	125	38	and	and	CCONJ
fcis-31893	125	39	mfcc	mfcc	NOUN
fcis-31893	125	40	,	,	PUNCT
fcis-31893	125	41	further	far	ADV
fcis-31893	125	42	enhances	enhance	VERB
fcis-31893	125	43	the	the	DET
fcis-31893	125	44	model	model	NOUN
fcis-31893	125	45	's	's	PART
fcis-31893	125	46	classification	classification	NOUN
fcis-31893	125	47	ability	ability	NOUN
fcis-31893	125	48	.	.	PUNCT
fcis-31893	126	1	future	future	ADJ
fcis-31893	126	2	research	research	NOUN
fcis-31893	126	3	can	can	AUX
fcis-31893	126	4	further	far	ADV
fcis-31893	126	5	optimize	optimize	VERB
fcis-31893	126	6	the	the	DET
fcis-31893	126	7	model	model	NOUN
fcis-31893	126	8	structure	structure	NOUN
fcis-31893	126	9	and	and	CCONJ
fcis-31893	126	10	parameters	parameter	NOUN
fcis-31893	126	11	to	to	PART
fcis-31893	126	12	achieve	achieve	VERB
fcis-31893	126	13	a	a	DET
fcis-31893	126	14	higher	high	ADJ
fcis-31893	126	15	level	level	NOUN
fcis-31893	126	16	of	of	ADP
fcis-31893	126	17	classification	classification	NOUN
fcis-31893	126	18	capability	capability	NOUN
fcis-31893	126	19	.	.	PUNCT
fcis-31893	127	1	4	4	X
fcis-31893	127	2	.	.	X
fcis-31893	127	3	conclusion	conclusion	NOUN
fcis-31893	127	4	this	this	DET
fcis-31893	127	5	article	article	NOUN
fcis-31893	127	6	focuses	focus	VERB
fcis-31893	127	7	on	on	ADP
fcis-31893	127	8	the	the	DET
fcis-31893	127	9	application	application	NOUN
fcis-31893	127	10	of	of	ADP
fcis-31893	127	11	audio	audio	ADJ
fcis-31893	127	12	analysis	analysis	NOUN
fcis-31893	127	13	technology	technology	NOUN
fcis-31893	127	14	in	in	ADP
fcis-31893	127	15	urban	urban	ADJ
fcis-31893	127	16	environmental	environmental	ADJ
fcis-31893	127	17	monitoring	monitoring	NOUN
fcis-31893	127	18	.	.	PUNCT
fcis-31893	128	1	to	to	PART
fcis-31893	128	2	address	address	VERB
fcis-31893	128	3	the	the	DET
fcis-31893	128	4	limitations	limitation	NOUN
fcis-31893	128	5	of	of	ADP
fcis-31893	128	6	current	current	ADJ
fcis-31893	128	7	audio	audio	ADJ
fcis-31893	128	8	classification	classification	NOUN
fcis-31893	128	9	tasks	task	NOUN
fcis-31893	128	10	in	in	ADP
fcis-31893	128	11	complex	complex	ADJ
fcis-31893	128	12	acoustic	acoustic	ADJ
fcis-31893	128	13	scenes	scene	NOUN
fcis-31893	128	14	,	,	PUNCT
fcis-31893	128	15	an	an	DET
fcis-31893	128	16	improved	improved	ADJ
fcis-31893	128	17	method	method	NOUN
fcis-31893	128	18	based	base	VERB
fcis-31893	128	19	on	on	ADP
fcis-31893	128	20	the	the	DET
fcis-31893	128	21	convnext	convnext	ADJ
fcis-31893	128	22	-	-	PUNCT
fcis-31893	128	23	feca	feca	NOUN
fcis-31893	128	24	model	model	NOUN
fcis-31893	128	25	is	be	AUX
fcis-31893	128	26	proposed	propose	VERB
fcis-31893	128	27	.	.	PUNCT
fcis-31893	129	1	the	the	DET
fcis-31893	129	2	main	main	ADJ
fcis-31893	129	3	contributions	contribution	NOUN
fcis-31893	129	4	of	of	ADP
fcis-31893	129	5	this	this	DET
fcis-31893	129	6	paper	paper	NOUN
fcis-31893	129	7	are	be	AUX
fcis-31893	129	8	reflected	reflect	VERB
fcis-31893	129	9	in	in	ADP
fcis-31893	129	10	several	several	ADJ
fcis-31893	129	11	aspects	aspect	NOUN
fcis-31893	129	12	:	:	PUNCT
fcis-31893	129	13	first	first	ADV
fcis-31893	129	14	,	,	PUNCT
fcis-31893	129	15	by	by	ADP
fcis-31893	129	16	combining	combine	VERB
fcis-31893	129	17	the	the	DET
fcis-31893	129	18	powerful	powerful	ADJ
fcis-31893	129	19	feature	feature	NOUN
fcis-31893	129	20	extraction	extraction	NOUN
fcis-31893	129	21	capability	capability	NOUN
fcis-31893	129	22	of	of	ADP
fcis-31893	129	23	convnext	convnext	NOUN
fcis-31893	129	24	with	with	ADP
fcis-31893	129	25	the	the	DET
fcis-31893	129	26	frequency	frequency	NOUN
fcis-31893	129	27	-	-	PUNCT
fcis-31893	129	28	domain	domain	NOUN
fcis-31893	129	29	enhanced	enhance	VERB
fcis-31893	129	30	attention	attention	NOUN
fcis-31893	129	31	mechanism	mechanism	NOUN
fcis-31893	129	32	(	(	PUNCT
fcis-31893	129	33	feca	feca	PROPN
fcis-31893	129	34	)	)	PUNCT
fcis-31893	129	35	,	,	PUNCT
fcis-31893	129	36	an	an	DET
fcis-31893	129	37	efficient	efficient	ADJ
fcis-31893	129	38	model	model	NOUN
fcis-31893	129	39	for	for	ADP
fcis-31893	129	40	environmental	environmental	ADJ
fcis-31893	129	41	audio	audio	NOUN
fcis-31893	129	42	classification	classification	NOUN
fcis-31893	129	43	,	,	PUNCT
fcis-31893	129	44	convnext	convnext	NOUN
fcis-31893	129	45	-	-	PUNCT
fcis-31893	129	46	feca	feca	NOUN
fcis-31893	129	47	,	,	PUNCT
fcis-31893	129	48	is	be	AUX
fcis-31893	129	49	designed	design	VERB
fcis-31893	129	50	.	.	PUNCT
fcis-31893	130	1	second	second	ADJ
fcis-31893	130	2	,	,	PUNCT
fcis-31893	130	3	through	through	ADP
fcis-31893	130	4	experiments	experiment	NOUN
fcis-31893	130	5	on	on	ADP
fcis-31893	130	6	the	the	DET
fcis-31893	130	7	urbansound8k	urbansound8k	NOUN
fcis-31893	130	8	dataset	dataset	NOUN
fcis-31893	130	9	,	,	PUNCT
fcis-31893	130	10	we	we	PRON
fcis-31893	130	11	systematically	systematically	ADV
fcis-31893	130	12	evaluated	evaluate	VERB
fcis-31893	130	13	the	the	DET
fcis-31893	130	14	performance	performance	NOUN
fcis-31893	130	15	of	of	ADP
fcis-31893	130	16	the	the	DET
fcis-31893	130	17	model	model	NOUN
fcis-31893	130	18	,	,	PUNCT
fcis-31893	130	19	and	and	CCONJ
fcis-31893	130	20	the	the	DET
fcis-31893	130	21	results	result	NOUN
fcis-31893	130	22	showed	show	VERB
fcis-31893	130	23	that	that	SCONJ
fcis-31893	130	24	it	it	PRON
fcis-31893	130	25	outperformed	outperform	VERB
fcis-31893	130	26	various	various	ADJ
fcis-31893	130	27	mainstream	mainstream	ADJ
fcis-31893	130	28	comparative	comparative	ADJ
fcis-31893	130	29	models	model	NOUN
fcis-31893	130	30	in	in	ADP
fcis-31893	130	31	terms	term	NOUN
fcis-31893	130	32	of	of	ADP
fcis-31893	130	33	classification	classification	NOUN
fcis-31893	130	34	accuracy	accuracy	NOUN
fcis-31893	130	35	,	,	PUNCT
fcis-31893	130	36	robustness	robustness	NOUN
fcis-31893	130	37	,	,	PUNCT
fcis-31893	130	38	and	and	CCONJ
fcis-31893	130	39	generalization	generalization	NOUN
fcis-31893	130	40	ability	ability	NOUN
fcis-31893	130	41	.	.	PUNCT
fcis-31893	131	1	additionally	additionally	ADV
fcis-31893	131	2	,	,	PUNCT
fcis-31893	131	3	this	this	DET
fcis-31893	131	4	paper	paper	NOUN
fcis-31893	131	5	further	far	ADV
fcis-31893	131	6	explores	explore	VERB
fcis-31893	131	7	the	the	DET
fcis-31893	131	8	impact	impact	NOUN
fcis-31893	131	9	of	of	ADP
fcis-31893	131	10	different	different	ADJ
fcis-31893	131	11	feature	feature	NOUN
fcis-31893	131	12	extraction	extraction	NOUN
fcis-31893	131	13	methods	method	NOUN
fcis-31893	131	14	and	and	CCONJ
fcis-31893	131	15	their	their	PRON
fcis-31893	131	16	fusion	fusion	NOUN
fcis-31893	131	17	approaches	approach	NOUN
fcis-31893	131	18	on	on	ADP
fcis-31893	131	19	model	model	NOUN
fcis-31893	131	20	performance	performance	NOUN
fcis-31893	131	21	,	,	PUNCT
fcis-31893	131	22	with	with	ADP
fcis-31893	131	23	experimental	experimental	ADJ
fcis-31893	131	24	results	result	NOUN
fcis-31893	131	25	indicating	indicate	VERB
fcis-31893	131	26	that	that	SCONJ
fcis-31893	131	27	the	the	DET
fcis-31893	131	28	spectrogram	spectrogram	NOUN
fcis-31893	131	29	mfcc	mfcc	NOUN
fcis-31893	131	30	feature	feature	NOUN
fcis-31893	131	31	fusion	fusion	NOUN
fcis-31893	131	32	method	method	NOUN
fcis-31893	131	33	has	have	VERB
fcis-31893	131	34	significant	significant	ADJ
fcis-31893	131	35	advantages	advantage	NOUN
fcis-31893	131	36	in	in	ADP
fcis-31893	131	37	capturing	capture	VERB
fcis-31893	131	38	time	time	NOUN
fcis-31893	131	39	-	-	PUNCT
fcis-31893	131	40	frequency	frequency	NOUN
fcis-31893	131	41	information	information	NOUN
fcis-31893	131	42	of	of	ADP
fcis-31893	131	43	audio	audio	ADJ
fcis-31893	131	44	signals	signal	NOUN
fcis-31893	131	45	,	,	PUNCT
fcis-31893	131	46	providing	provide	VERB
fcis-31893	131	47	a	a	DET
fcis-31893	131	48	new	new	ADJ
fcis-31893	131	49	perspective	perspective	NOUN
fcis-31893	131	50	for	for	ADP
fcis-31893	131	51	feature	feature	NOUN
fcis-31893	131	52	modeling	modeling	NOUN
fcis-31893	131	53	of	of	ADP
fcis-31893	131	54	complex	complex	ADJ
fcis-31893	131	55	audio	audio	ADJ
fcis-31893	131	56	data	datum	NOUN
fcis-31893	131	57	.	.	PUNCT
fcis-31893	132	1	however	however	ADV
fcis-31893	132	2	,	,	PUNCT
fcis-31893	132	3	this	this	DET
fcis-31893	132	4	study	study	NOUN
fcis-31893	132	5	still	still	ADV
fcis-31893	132	6	has	have	VERB
fcis-31893	132	7	certain	certain	ADJ
fcis-31893	132	8	limitations	limitation	NOUN
fcis-31893	132	9	.	.	PUNCT
fcis-31893	133	1	on	on	ADP
fcis-31893	133	2	one	one	NUM
fcis-31893	133	3	hand	hand	NOUN
fcis-31893	133	4	,	,	PUNCT
fcis-31893	133	5	the	the	DET
fcis-31893	133	6	current	current	ADJ
fcis-31893	133	7	experiments	experiment	NOUN
fcis-31893	133	8	are	be	AUX
fcis-31893	133	9	mainly	mainly	ADV
fcis-31893	133	10	focused	focus	VERB
fcis-31893	133	11	on	on	ADP
fcis-31893	133	12	the	the	DET
fcis-31893	133	13	urbansound8k	urbansound8k	NOUN
fcis-31893	133	14	dataset	dataset	NOUN
fcis-31893	133	15	,	,	PUNCT
fcis-31893	133	16	which	which	PRON
fcis-31893	133	17	,	,	PUNCT
fcis-31893	133	18	although	although	SCONJ
fcis-31893	133	19	it	it	PRON
fcis-31893	133	20	covers	cover	VERB
fcis-31893	133	21	a	a	DET
fcis-31893	133	22	variety	variety	NOUN
fcis-31893	133	23	of	of	ADP
fcis-31893	133	24	common	common	ADJ
fcis-31893	133	25	urban	urban	ADJ
fcis-31893	133	26	environmental	environmental	ADJ
fcis-31893	133	27	sounds	sound	NOUN
fcis-31893	133	28	,	,	PUNCT
fcis-31893	133	29	needs	need	VERB
fcis-31893	133	30	further	further	ADJ
fcis-31893	133	31	validation	validation	NOUN
fcis-31893	133	32	for	for	ADP
fcis-31893	133	33	applicability	applicability	NOUN
fcis-31893	133	34	on	on	ADP
fcis-31893	133	35	larger	large	ADJ
fcis-31893	133	36	scale	scale	NOUN
fcis-31893	133	37	and	and	CCONJ
fcis-31893	133	38	more	more	ADV
fcis-31893	133	39	complex	complex	ADJ
fcis-31893	133	40	45	45	NUM
fcis-31893	133	41	scene	scene	NOUN
fcis-31893	133	42	datasets	dataset	NOUN
fcis-31893	133	43	;	;	PUNCT
fcis-31893	133	44	on	on	ADP
fcis-31893	133	45	the	the	DET
fcis-31893	133	46	other	other	ADJ
fcis-31893	133	47	hand	hand	NOUN
fcis-31893	133	48	,	,	PUNCT
fcis-31893	133	49	feature	feature	NOUN
fcis-31893	133	50	fusion	fusion	NOUN
fcis-31893	133	51	methods	method	NOUN
fcis-31893	133	52	may	may	AUX
fcis-31893	133	53	have	have	VERB
fcis-31893	133	54	certain	certain	ADJ
fcis-31893	133	55	burdens	burden	NOUN
fcis-31893	133	56	in	in	ADP
fcis-31893	133	57	terms	term	NOUN
fcis-31893	133	58	of	of	ADP
fcis-31893	133	59	computational	computational	ADJ
fcis-31893	133	60	costs	cost	NOUN
fcis-31893	133	61	,	,	PUNCT
fcis-31893	133	62	and	and	CCONJ
fcis-31893	133	63	how	how	SCONJ
fcis-31893	133	64	to	to	PART
fcis-31893	133	65	optimize	optimize	VERB
fcis-31893	133	66	model	model	NOUN
fcis-31893	133	67	computational	computational	ADJ
fcis-31893	133	68	efficiency	efficiency	NOUN
fcis-31893	133	69	while	while	SCONJ
fcis-31893	133	70	improving	improve	VERB
fcis-31893	133	71	performance	performance	NOUN
fcis-31893	133	72	is	be	AUX
fcis-31893	133	73	a	a	DET
fcis-31893	133	74	direction	direction	NOUN
fcis-31893	133	75	that	that	PRON
fcis-31893	133	76	requires	require	VERB
fcis-31893	133	77	in	in	ADP
fcis-31893	133	78	-	-	PUNCT
fcis-31893	133	79	depth	depth	NOUN
fcis-31893	133	80	research	research	NOUN
fcis-31893	133	81	in	in	ADP
fcis-31893	133	82	the	the	DET
fcis-31893	133	83	future	future	NOUN
fcis-31893	133	84	.	.	PUNCT
fcis-31893	134	1	additionally	additionally	ADV
fcis-31893	134	2	,	,	PUNCT
fcis-31893	134	3	audio	audio	ADJ
fcis-31893	134	4	analysis	analysis	NOUN
fcis-31893	134	5	technology	technology	NOUN
fcis-31893	134	6	also	also	ADV
fcis-31893	134	7	needs	need	VERB
fcis-31893	134	8	to	to	PART
fcis-31893	134	9	address	address	VERB
fcis-31893	134	10	various	various	ADJ
fcis-31893	134	11	non	non	ADJ
fcis-31893	134	12	-	-	ADJ
fcis-31893	134	13	stationary	stationary	ADJ
fcis-31893	134	14	noises	noise	NOUN
fcis-31893	134	15	and	and	CCONJ
fcis-31893	134	16	dynamic	dynamic	ADJ
fcis-31893	134	17	environmental	environmental	ADJ
fcis-31893	134	18	changes	change	NOUN
fcis-31893	134	19	in	in	ADP
fcis-31893	134	20	practical	practical	ADJ
fcis-31893	134	21	environmental	environmental	ADJ
fcis-31893	134	22	monitoring	monitoring	NOUN
fcis-31893	134	23	applications	application	NOUN
fcis-31893	134	24	,	,	PUNCT
fcis-31893	134	25	which	which	PRON
fcis-31893	134	26	raises	raise	VERB
fcis-31893	134	27	higher	high	ADJ
fcis-31893	134	28	requirements	requirement	NOUN
fcis-31893	134	29	for	for	ADP
fcis-31893	134	30	the	the	DET
fcis-31893	134	31	model	model	NOUN
fcis-31893	134	32	's	's	PART
fcis-31893	134	33	real	real	ADJ
fcis-31893	134	34	-	-	PUNCT
fcis-31893	134	35	time	time	NOUN
fcis-31893	134	36	performance	performance	NOUN
fcis-31893	134	37	and	and	CCONJ
fcis-31893	134	38	robustness	robustness	NOUN
fcis-31893	134	39	.	.	PUNCT
fcis-31893	135	1	references	reference	NOUN
fcis-31893	135	2	[	[	X
fcis-31893	135	3	1	1	NUM
fcis-31893	135	4	]	]	X
fcis-31893	135	5	chen	chen	PROPN
fcis-31893	135	6	x	x	PROPN
fcis-31893	135	7	,	,	PUNCT
fcis-31893	135	8	wang	wang	PROPN
fcis-31893	135	9	m	m	PROPN
fcis-31893	135	10	,	,	PUNCT
fcis-31893	135	11	kan	kan	PROPN
fcis-31893	135	12	r	r	PROPN
fcis-31893	135	13	,	,	PUNCT
fcis-31893	135	14	et	et	PROPN
fcis-31893	135	15	al	al	PROPN
fcis-31893	135	16	.	.	PROPN
fcis-31893	135	17	improved	improve	VERB
fcis-31893	135	18	patch	patch	NOUN
fcis-31893	135	19	-	-	PUNCT
fcis-31893	135	20	mix	mix	NOUN
fcis-31893	135	21	transformer	transformer	NOUN
fcis-31893	135	22	sound	sound	NOUN
fcis-31893	135	23	classification	classification	NOUN
fcis-31893	135	24	in	in	ADP
fcis-31893	135	25	noisy	noisy	ADJ
fcis-31893	135	26	environments	environment	NOUN
fcis-31893	135	27	using	use	VERB
fcis-31893	135	28	contrastive	contrastive	ADJ
fcis-31893	135	29	learning	learning	NOUN
fcis-31893	135	30	methods	method	NOUN
fcis-31893	135	31	[	[	X
fcis-31893	135	32	j	j	X
fcis-31893	135	33	]	]	X
fcis-31893	135	34	.	.	PUNCT
fcis-31893	136	1	applied	apply	VERB
fcis-31893	136	2	sciences	science	NOUN
fcis-31893	136	3	,	,	PUNCT
fcis-31893	136	4	2024	2024	NUM
fcis-31893	136	5	,	,	PUNCT
fcis-31893	136	6	14	14	NUM
fcis-31893	136	7	(	(	PUNCT
fcis-31893	136	8	21	21	NUM
fcis-31893	136	9	):	):	PUNCT
fcis-31893	136	10	9711	9711	NUM
fcis-31893	136	11	-	-	SYM
fcis-31893	136	12	9711	9711	NUM
fcis-31893	136	13	.	.	PUNCT
fcis-31893	137	1	[	[	X
fcis-31893	137	2	2	2	NUM
fcis-31893	137	3	]	]	X
fcis-31893	137	4	talukder	talukder	NOUN
fcis-31893	137	5	a	a	DET
fcis-31893	137	6	m	m	PROPN
fcis-31893	137	7	,	,	PUNCT
fcis-31893	137	8	khalid	khalid	PROPN
fcis-31893	137	9	m	m	PROPN
fcis-31893	137	10	,	,	PUNCT
fcis-31893	137	11	kazi	kazi	PROPN
fcis-31893	137	12	m	m	PROPN
fcis-31893	137	13	,	,	PUNCT
fcis-31893	137	14	et	et	PROPN
fcis-31893	137	15	al	al	PROPN
fcis-31893	137	16	.	.	PUNCT
fcis-31893	138	1	a	a	DET
fcis-31893	138	2	hybrid	hybrid	ADJ
fcis-31893	138	3	cardiovascular	cardiovascular	ADJ
fcis-31893	138	4	arrhythmia	arrhythmia	NOUN
fcis-31893	138	5	disease	disease	NOUN
fcis-31893	138	6	detection	detection	NOUN
fcis-31893	138	7	using	use	VERB
fcis-31893	138	8	convnextx	convnextx	NOUN
fcis-31893	138	9	models	model	NOUN
fcis-31893	138	10	on	on	ADP
fcis-31893	138	11	electrocardiogram	electrocardiogram	NOUN
fcis-31893	138	12	signals	signal	NOUN
fcis-31893	138	13	.	.	PUNCT
fcis-31893	139	1	[	[	X
fcis-31893	139	2	j	j	X
fcis-31893	139	3	]	]	X
fcis-31893	139	4	.	.	PUNCT
fcis-31893	140	1	scientific	scientific	ADJ
fcis-31893	140	2	reports	report	NOUN
fcis-31893	140	3	,	,	PUNCT
fcis-31893	140	4	2024	2024	NUM
fcis-31893	140	5	,	,	PUNCT
fcis-31893	140	6	14(1):30366	14(1):30366	NUM
fcis-31893	140	7	.	.	PUNCT
fcis-31893	141	1	[	[	X
fcis-31893	141	2	3	3	X
fcis-31893	141	3	]	]	X
fcis-31893	141	4	huang	huang	PROPN
fcis-31893	141	5	wenbo	wenbo	PROPN
fcis-31893	141	6	,	,	PUNCT
fcis-31893	141	7	huang	huang	PROPN
fcis-31893	141	8	yuxiang	yuxiang	PROPN
fcis-31893	141	9	,	,	PUNCT
fcis-31893	141	10	yao	yao	PROPN
fcis-31893	141	11	yuan	yuan	PROPN
fcis-31893	141	12	,	,	PUNCT
fcis-31893	141	13	et	et	PROPN
fcis-31893	141	14	al	al	PROPN
fcis-31893	141	15	.	.	PROPN
fcis-31893	142	1	automatic	automatic	ADJ
fcis-31893	142	2	grading	grading	NOUN
fcis-31893	142	3	of	of	ADP
fcis-31893	142	4	convnext	convnext	ADJ
fcis-31893	142	5	retinopathy	retinopathy	NOUN
fcis-31893	142	6	fused	fuse	VERB
fcis-31893	142	7	with	with	ADP
fcis-31893	142	8	attention	attention	NOUN
fcis-31893	142	9	[	[	X
fcis-31893	142	10	j	j	X
fcis-31893	142	11	]	]	X
fcis-31893	142	12	.	.	PUNCT
fcis-31893	143	1	optics	optic	NOUN
fcis-31893	143	2	and	and	CCONJ
fcis-31893	143	3	precision	precision	NOUN
fcis-31893	143	4	engineering	engineering	NOUN
fcis-31893	143	5	,	,	PUNCT
fcis-31893	143	6	2022,30(17):2147	2022,30(17):2147	NUM
fcis-31893	143	7	-	-	SYM
fcis-31893	143	8	2154	2154	NUM
fcis-31893	143	9	.	.	PUNCT
fcis-31893	144	1	[	[	X
fcis-31893	144	2	4	4	X
fcis-31893	144	3	]	]	X
fcis-31893	144	4	wang	wang	PROPN
fcis-31893	144	5	j	j	PROPN
fcis-31893	144	6	,	,	PUNCT
fcis-31893	144	7	zhang	zhang	PROPN
fcis-31893	144	8	b	b	PROPN
fcis-31893	144	9	,	,	PUNCT
fcis-31893	144	10	yin	yin	PROPN
fcis-31893	144	11	d	d	PROPN
fcis-31893	144	12	,	,	PUNCT
fcis-31893	144	13	et	et	PROPN
fcis-31893	144	14	al	al	PROPN
fcis-31893	144	15	.	.	PUNCT
fcis-31893	144	16	distribution	distribution	NOUN
fcis-31893	144	17	network	network	NOUN
fcis-31893	144	18	fault	fault	NOUN
fcis-31893	144	19	identification	identification	NOUN
fcis-31893	144	20	method	method	NOUN
fcis-31893	144	21	based	base	VERB
fcis-31893	144	22	on	on	ADP
fcis-31893	144	23	multimodal	multimodal	NOUN
fcis-31893	144	24	resnet	resnet	NOUN
fcis-31893	144	25	with	with	ADP
fcis-31893	144	26	recorded	record	VERB
fcis-31893	144	27	waveform	waveform	NOUN
fcis-31893	144	28	-	-	PUNCT
fcis-31893	144	29	driven	drive	VERB
fcis-31893	144	30	feature	feature	NOUN
fcis-31893	144	31	extraction[j	extraction[j	PROPN
fcis-31893	144	32	]	]	PUNCT
fcis-31893	144	33	.	.	PUNCT
fcis-31893	145	1	energy	energy	NOUN
fcis-31893	145	2	reports	report	NOUN
fcis-31893	145	3	,	,	PUNCT
fcis-31893	145	4	2025	2025	NUM
fcis-31893	145	5	,	,	PUNCT
fcis-31893	145	6	1390	1390	NUM
fcis-31893	145	7	-	-	SYM
fcis-31893	145	8	104	104	NUM
fcis-31893	145	9	.	.	PUNCT
fcis-31893	146	1	[	[	X
fcis-31893	146	2	5	5	NUM
fcis-31893	146	3	]	]	PUNCT
fcis-31893	146	4	lozoya	lozoya	PROPN
fcis-31893	146	5	l	l	PROPN
fcis-31893	146	6	s	s	PART
fcis-31893	146	7	r	r	NOUN
fcis-31893	146	8	,	,	PUNCT
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fcis-31893	146	10	o	o	X
fcis-31893	146	11	j	j	PROPN
fcis-31893	147	1	d	d	PROPN
fcis-31893	147	2	h	h	PROPN
fcis-31893	147	3	,	,	PUNCT
fcis-31893	147	4	azuela	azuela	PROPN
fcis-31893	147	5	s	s	PROPN
fcis-31893	147	6	h	h	PROPN
fcis-31893	147	7	j	j	PROPN
fcis-31893	147	8	,	,	PUNCT
fcis-31893	147	9	et	et	PROPN
fcis-31893	147	10	al	al	PROPN
fcis-31893	147	11	.	.	PUNCT
fcis-31893	148	1	residual	residual	ADJ
fcis-31893	148	2	shallow	shallow	ADJ
fcis-31893	148	3	convolutional	convolutional	ADJ
fcis-31893	148	4	neural	neural	ADJ
fcis-31893	148	5	network	network	NOUN
fcis-31893	148	6	to	to	PART
fcis-31893	148	7	classify	classify	VERB
fcis-31893	148	8	microcalcifications	microcalcification	NOUN
fcis-31893	148	9	clusters	cluster	NOUN
fcis-31893	148	10	in	in	ADP
fcis-31893	148	11	digital	digital	PROPN
fcis-31893	148	12	mammograms[j	mammograms[j	PROPN
fcis-31893	148	13	]	]	X
fcis-31893	148	14	.	.	PUNCT
fcis-31893	149	1	biomedical	biomedical	ADJ
fcis-31893	149	2	signal	signal	NOUN
fcis-31893	149	3	processing	processing	NOUN
fcis-31893	149	4	and	and	CCONJ
fcis-31893	149	5	control,2025,102107209107209	control,2025,102107209107209	NOUN
fcis-31893	149	6	.	.	PUNCT
fcis-31893	150	1	[	[	X
fcis-31893	150	2	6	6	NUM
fcis-31893	150	3	]	]	PUNCT
fcis-31893	150	4	schmidt	schmidt	PROPN
fcis-31893	150	5	a	a	DET
fcis-31893	150	6	,	,	PUNCT
fcis-31893	150	7	gierden	gierden	PROPN
fcis-31893	150	8	c	c	NOUN
fcis-31893	150	9	,	,	PUNCT
fcis-31893	150	10	heinen	heinen	NOUN
fcis-31893	150	11	f	f	NOUN
fcis-31893	150	12	r	r	NOUN
fcis-31893	150	13	,	,	PUNCT
fcis-31893	150	14	et	et	NOUN
fcis-31893	150	15	al.efficient	al.efficient	NOUN
fcis-31893	150	16	thermomechanically	thermomechanically	ADV
fcis-31893	150	17	coupled	couple	VERB
fcis-31893	150	18	and	and	CCONJ
fcis-31893	150	19	geometrically	geometrically	ADV
fcis-31893	150	20	nonlinear	nonlinear	VERB
fcis-31893	150	21	two	two	NUM
fcis-31893	150	22	-	-	PUNCT
fcis-31893	150	23	scale	scale	NOUN
fcis-31893	150	24	fe	fe	NOUN
fcis-31893	150	25	-	-	PUNCT
fcis-31893	150	26	fft	fft	NOUN
fcis-31893	150	27	-	-	PUNCT
fcis-31893	150	28	based	base	VERB
fcis-31893	150	29	modeling	modeling	NOUN
fcis-31893	150	30	of	of	ADP
fcis-31893	150	31	elasto	elasto	NOUN
fcis-31893	150	32	-	-	PUNCT
fcis-31893	150	33	viscoplastic	viscoplastic	ADJ
fcis-31893	150	34	polycrystalline	polycrystalline	ADJ
fcis-31893	150	35	materials[j].computer	materials[j].computer	ADJ
fcis-31893	150	36	methods	method	NOUN
fcis-31893	150	37	in	in	ADP
fcis-31893	150	38	applied	applied	ADJ
fcis-31893	150	39	mechanics	mechanic	NOUN
fcis-31893	150	40	and	and	CCONJ
fcis-31893	150	41	engineering,2025,435117648	engineering,2025,435117648	NOUN
fcis-31893	150	42	-	-	PROPN
fcis-31893	150	43	117648	117648	NUM
fcis-31893	150	44	.	.	PUNCT
fcis-31893	151	1	[	[	X
fcis-31893	151	2	7	7	X
fcis-31893	151	3	]	]	X
fcis-31893	151	4	hantao	hantao	PROPN
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fcis-31893	151	6	,	,	PUNCT
fcis-31893	151	7	xin	xin	PROPN
fcis-31893	151	8	g	g	PROPN
fcis-31893	151	9	,	,	PUNCT
fcis-31893	151	10	hualei	hualei	NOUN
fcis-31893	151	11	x	x	X
fcis-31893	151	12	,	,	PUNCT
fcis-31893	151	13	et	et	PROPN
fcis-31893	151	14	al.comprehensive	al.comprehensive	VERB
fcis-31893	151	15	receptive	receptive	ADJ
fcis-31893	151	16	field	field	NOUN
fcis-31893	151	17	adaptive	adaptive	ADJ
fcis-31893	151	18	graph	graph	NOUN
fcis-31893	151	19	convolutional	convolutional	ADJ
fcis-31893	151	20	networks	network	NOUN
fcis-31893	151	21	for	for	ADP
fcis-31893	151	22	action	action	NOUN
fcis-31893	151	23	recognition[j].journal	recognition[j].journal	ADJ
fcis-31893	151	24	of	of	ADP
fcis-31893	151	25	visual	visual	ADJ
fcis-31893	151	26	communication	communication	NOUN
fcis-31893	151	27	and	and	CCONJ
fcis-31893	151	28	image	image	NOUN
fcis-31893	151	29	representation,2023,97	representation,2023,97	NOUN
fcis-31893	151	30	.	.	PUNCT
fcis-31893	152	1	[	[	X
fcis-31893	152	2	8	8	NUM
fcis-31893	152	3	]	]	X
fcis-31893	152	4	chen	chen	PROPN
fcis-31893	152	5	y	y	PROPN
fcis-31893	152	6	,	,	PUNCT
fcis-31893	152	7	zheng	zheng	PROPN
fcis-31893	152	8	s	s	PROPN
fcis-31893	152	9	,	,	PUNCT
fcis-31893	152	10	wang	wang	PROPN
fcis-31893	152	11	h	h	PROPN
fcis-31893	152	12	,	,	PUNCT
fcis-31893	152	13	et	et	PROPN
fcis-31893	152	14	al.eres2netv2	al.eres2netv2	PROPN
fcis-31893	152	15	:	:	PUNCT
fcis-31893	152	16	boosting	boost	VERB
fcis-31893	152	17	short	short	ADJ
fcis-31893	152	18	-	-	PUNCT
fcis-31893	152	19	duration	duration	NOUN
fcis-31893	152	20	speaker	speaker	NOUN
fcis-31893	152	21	verification	verification	NOUN
fcis-31893	152	22	performance	performance	NOUN
fcis-31893	152	23	with	with	ADP
fcis-31893	152	24	computational	computational	ADJ
fcis-31893	152	25	efficiency[j	efficiency[j	NOUN
fcis-31893	152	26	]	]	PUNCT
fcis-31893	152	27	.	.	PUNCT
fcis-31893	153	1	arxiv	arxiv	PROPN
fcis-31893	153	2	preprint	preprint	PROPN
fcis-31893	153	3	arxiv	arxiv	PROPN
fcis-31893	153	4	:	:	PUNCT
fcis-31893	153	5	2406	2406	NUM
fcis-31893	153	6	.	.	PUNCT
fcis-31893	154	1	02167	02167	NUM
fcis-31893	154	2	(	(	PUNCT
fcis-31893	154	3	2024	2024	NUM
fcis-31893	154	4	)	)	PUNCT
fcis-31893	154	5	.	.	PUNCT
fcis-31893	155	1	[	[	X
fcis-31893	155	2	9	9	X
fcis-31893	155	3	]	]	X
fcis-31893	155	4	curtis	curtis	PROPN
fcis-31893	155	5	j.	j.	PROPN
fcis-31893	155	6	keegan	keegan	PROPN
fcis-31893	155	7	issues	issues	PROPN
fcis-31893	155	8	warning	warn	VERB
fcis-31893	155	9	to	to	ADP
fcis-31893	155	10	players	player	NOUN
fcis-31893	155	11	;	;	PUNCT
fcis-31893	155	12	football	football	NOUN
fcis-31893	155	13	:	:	PUNCT
fcis-31893	155	14	latest	late	ADJ
fcis-31893	155	15	from	from	ADP
fcis-31893	155	16	the	the	DET
fcis-31893	155	17	england	england	PROPN
fcis-31893	155	18	camp	camp	PROPN
fcis-31893	155	19	plus	plus	CCONJ
fcis-31893	155	20	local	local	ADJ
fcis-31893	155	21	round	round	ADJ
fcis-31893	155	22	-	-	PUNCT
fcis-31893	155	23	up[j].[2024	up[j].[2024	NOUN
fcis-31893	155	24	-	-	SYM
fcis-31893	155	25	12	12	NUM
fcis-31893	155	26	-	-	SYM
fcis-31893	155	27	25	25	NUM
fcis-31893	155	28	]	]	PUNCT
fcis-31893	155	29	.	.	PUNCT
fcis-31893	156	1	[	[	X
fcis-31893	156	2	10	10	NUM
fcis-31893	156	3	]	]	X
fcis-31893	156	4	ning	ning	NOUN
fcis-31893	156	5	l	l	PROPN
fcis-31893	156	6	,	,	PUNCT
fcis-31893	156	7	weina	weina	PROPN
fcis-31893	156	8	j	j	PROPN
fcis-31893	156	9	,	,	PUNCT
fcis-31893	156	10	xia	xia	PROPN
fcis-31893	156	11	l	l	PROPN
fcis-31893	156	12	,	,	PUNCT
fcis-31893	156	13	et	et	NOUN
fcis-31893	156	14	al.a	al.a	VERB
fcis-31893	156	15	high	high	ADJ
fcis-31893	156	16	mechanical	mechanical	ADJ
fcis-31893	156	17	strength	strength	NOUN
fcis-31893	156	18	,	,	PUNCT
fcis-31893	156	19	deformable	deformable	ADJ
fcis-31893	156	20	,	,	PUNCT
fcis-31893	156	21	fatigue	fatigue	NOUN
fcis-31893	156	22	-	-	PUNCT
fcis-31893	156	23	resistant	resistant	ADJ
fcis-31893	156	24	polyacrylonitrile	polyacrylonitrile	ADJ
fcis-31893	156	25	nanospherereinforced	nanospherereinforce	VERB
fcis-31893	156	26	gel	gel	NOUN
fcis-31893	156	27	electrolyte	electrolyte	NOUN
fcis-31893	156	28	for	for	ADP
fcis-31893	156	29	supercapacitors[j].chemical	supercapacitors[j].chemical	ADJ
fcis-31893	156	30	engineering	engineering	NOUN
fcis-31893	156	31	journal,2023,474	journal,2023,474	PROPN
fcis-31893	156	32	.	.	PUNCT
fcis-31893	157	1	[	[	X
fcis-31893	157	2	11	11	NUM
fcis-31893	157	3	]	]	PUNCT
fcis-31893	157	4	yang	yang	PROPN
fcis-31893	157	5	junjie	junjie	PROPN
fcis-31893	157	6	,	,	PUNCT
fcis-31893	157	7	ding	ding	PROPN
fcis-31893	157	8	jiahui	jiahui	PROPN
fcis-31893	157	9	,	,	PUNCT
fcis-31893	157	10	weng	weng	PROPN
fcis-31893	157	11	shilong	shilong	PROPN
fcis-31893	157	12	,	,	PUNCT
fcis-31893	157	13	et	et	PROPN
fcis-31893	157	14	al	al	PROPN
fcis-31893	157	15	.	.	PUNCT
fcis-31893	158	1	an	an	DET
fcis-31893	158	2	automatic	automatic	ADJ
fcis-31893	158	3	classification	classification	NOUN
fcis-31893	158	4	method	method	NOUN
fcis-31893	158	5	for	for	ADP
fcis-31893	158	6	indoor	indoor	ADJ
fcis-31893	158	7	environmental	environmental	ADJ
fcis-31893	158	8	sounds	sound	NOUN
fcis-31893	158	9	based	base	VERB
fcis-31893	158	10	on	on	ADP
fcis-31893	158	11	lightweight	lightweight	ADJ
fcis-31893	158	12	ecapa	ecapa	VERB
fcis-31893	158	13	-	-	PUNCT
fcis-31893	158	14	tdnn	tdnn	NOUN
fcis-31893	158	15	neural	neural	ADJ
fcis-31893	158	16	network	network	NOUN
fcis-31893	158	17	:	:	PUNCT
fcis-31893	158	18	cn2022117	cn2022117	PROPN
fcis-31893	158	19	15093.7[p].cn116013276a[2024	15093.7[p].cn116013276a[2024	NUM
fcis-31893	158	20	-	-	SYM
fcis-31893	158	21	12	12	NUM
fcis-31893	158	22	-	-	SYM
fcis-31893	158	23	25	25	NUM
fcis-31893	158	24	]	]	PUNCT
fcis-31893	158	25	.	.	PUNCT