id	sid	tid	token	lemma	pos
ajst-20786	1	1	academic	academic	ADJ
ajst-20786	1	2	journal	journal	NOUN
ajst-20786	1	3	of	of	ADP
ajst-20786	1	4	science	science	NOUN
ajst-20786	1	5	and	and	CCONJ
ajst-20786	1	6	technology	technology	NOUN
ajst-20786	1	7	issn	issn	NOUN
ajst-20786	1	8	:	:	PUNCT
ajst-20786	1	9	2771	2771	NUM
ajst-20786	1	10	-	-	SYM
ajst-20786	1	11	3032	3032	NUM
ajst-20786	1	12	|	|	NOUN
ajst-20786	1	13	vol	vol	NOUN
ajst-20786	1	14	.	.	PROPN
ajst-20786	2	1	10	10	NUM
ajst-20786	2	2	,	,	PUNCT
ajst-20786	2	3	no	no	INTJ
ajst-20786	2	4	.	.	NOUN
ajst-20786	2	5	3	3	NUM
ajst-20786	2	6	,	,	PUNCT
ajst-20786	2	7	2024	2024	NUM
ajst-20786	2	8	74	74	NUM
ajst-20786	2	9	comparison	comparison	NOUN
ajst-20786	2	10	among	among	ADP
ajst-20786	2	11	alexnet	alexnet	NOUN
ajst-20786	2	12	,	,	PUNCT
ajst-20786	2	13	googlenet	googlenet	NOUN
ajst-20786	2	14	and	and	CCONJ
ajst-20786	2	15	resnet‐18	resnet‐18	VERB
ajst-20786	2	16	in	in	ADP
ajst-20786	2	17	autiomatic	autiomatic	ADJ
ajst-20786	2	18	music	music	NOUN
ajst-20786	2	19	genre	genre	NOUN
ajst-20786	2	20	classification	classification	NOUN
ajst-20786	2	21	xinyu	xinyu	PROPN
ajst-20786	2	22	hong1	hong1	PROPN
ajst-20786	2	23	,	,	PUNCT
ajst-20786	2	24	a	a	DET
ajst-20786	2	25	1maynooth	1maynooth	NUM
ajst-20786	2	26	international	international	ADJ
ajst-20786	2	27	engineering	engineering	NOUN
ajst-20786	2	28	college	college	PROPN
ajst-20786	2	29	,	,	PUNCT
ajst-20786	2	30	fuzhou	fuzhou	PROPN
ajst-20786	2	31	university	university	PROPN
ajst-20786	2	32	,	,	PUNCT
ajst-20786	2	33	fuzhou	fuzhou	PROPN
ajst-20786	2	34	,	,	PUNCT
ajst-20786	2	35	350000	350000	NUM
ajst-20786	2	36	,	,	PUNCT
ajst-20786	2	37	china	china	PROPN
ajst-20786	2	38	axinyu.hong.2023@mumail.ie	axinyu.hong.2023@mumail.ie	PROPN
ajst-20786	2	39	abstract	abstract	PROPN
ajst-20786	2	40	:	:	PUNCT
ajst-20786	2	41	this	this	DET
ajst-20786	2	42	paper	paper	NOUN
ajst-20786	2	43	compared	compare	VERB
ajst-20786	2	44	the	the	DET
ajst-20786	2	45	performance	performance	NOUN
ajst-20786	2	46	among	among	ADP
ajst-20786	2	47	three	three	NUM
ajst-20786	2	48	famous	famous	ADJ
ajst-20786	2	49	convolutional	convolutional	ADJ
ajst-20786	2	50	neural	neural	ADJ
ajst-20786	2	51	networks	network	NOUN
ajst-20786	2	52	in	in	ADP
ajst-20786	2	53	classifying	classify	VERB
ajst-20786	2	54	music	music	NOUN
ajst-20786	2	55	genres	genre	NOUN
ajst-20786	2	56	.	.	PUNCT
ajst-20786	3	1	being	be	AUX
ajst-20786	3	2	carried	carry	VERB
ajst-20786	3	3	out	out	ADP
ajst-20786	3	4	on	on	ADP
ajst-20786	3	5	a	a	DET
ajst-20786	3	6	prominent	prominent	ADJ
ajst-20786	3	7	dataset	dataset	NOUN
ajst-20786	3	8	named	name	VERB
ajst-20786	3	9	free	free	ADJ
ajst-20786	3	10	music	music	NOUN
ajst-20786	3	11	archive	archive	NOUN
ajst-20786	3	12	,	,	PUNCT
ajst-20786	3	13	the	the	DET
ajst-20786	3	14	experiments	experiment	NOUN
ajst-20786	3	15	show	show	VERB
ajst-20786	3	16	that	that	SCONJ
ajst-20786	3	17	resnet-18	resnet-18	PROPN
ajst-20786	3	18	performs	perform	VERB
ajst-20786	3	19	much	much	ADV
ajst-20786	3	20	better	well	ADJ
ajst-20786	3	21	than	than	ADP
ajst-20786	3	22	alexnet	alexnet	NOUN
ajst-20786	3	23	and	and	CCONJ
ajst-20786	3	24	googlenet	googlenet	NOUN
ajst-20786	3	25	in	in	ADP
ajst-20786	3	26	classifying	classify	VERB
ajst-20786	3	27	the	the	DET
ajst-20786	3	28	music	music	NOUN
ajst-20786	3	29	genres	genre	NOUN
ajst-20786	3	30	in	in	ADP
ajst-20786	3	31	a	a	DET
ajst-20786	3	32	relatively	relatively	ADV
ajst-20786	3	33	small	small	ADJ
ajst-20786	3	34	dataset	dataset	NOUN
ajst-20786	3	35	.	.	PUNCT
ajst-20786	4	1	meanwhile	meanwhile	ADV
ajst-20786	4	2	,	,	PUNCT
ajst-20786	4	3	the	the	DET
ajst-20786	4	4	classification	classification	NOUN
ajst-20786	4	5	accuracy	accuracy	NOUN
ajst-20786	4	6	of	of	ADP
ajst-20786	4	7	each	each	DET
ajst-20786	4	8	model	model	NOUN
ajst-20786	4	9	for	for	ADP
ajst-20786	4	10	each	each	DET
ajst-20786	4	11	music	music	NOUN
ajst-20786	4	12	genre	genre	NOUN
ajst-20786	4	13	was	be	AUX
ajst-20786	4	14	also	also	ADV
ajst-20786	4	15	recorded	record	VERB
ajst-20786	4	16	.	.	PUNCT
ajst-20786	5	1	it	it	PRON
ajst-20786	5	2	indicates	indicate	VERB
ajst-20786	5	3	that	that	SCONJ
ajst-20786	5	4	different	different	ADJ
ajst-20786	5	5	models	model	NOUN
ajst-20786	5	6	could	could	AUX
ajst-20786	5	7	be	be	AUX
ajst-20786	5	8	expert	expert	ADJ
ajst-20786	5	9	in	in	ADP
ajst-20786	5	10	identifying	identify	VERB
ajst-20786	5	11	distinct	distinct	ADJ
ajst-20786	5	12	genres	genre	NOUN
ajst-20786	5	13	.	.	PUNCT
ajst-20786	6	1	several	several	ADJ
ajst-20786	6	2	genres	genre	NOUN
ajst-20786	6	3	,	,	PUNCT
ajst-20786	6	4	including	include	VERB
ajst-20786	6	5	blues	blue	NOUN
ajst-20786	6	6	,	,	PUNCT
ajst-20786	6	7	hip	hip	NOUN
ajst-20786	6	8	-	-	PUNCT
ajst-20786	6	9	hop	hop	NOUN
ajst-20786	6	10	and	and	CCONJ
ajst-20786	6	11	international	international	ADJ
ajst-20786	6	12	,	,	PUNCT
ajst-20786	6	13	were	be	AUX
ajst-20786	6	14	not	not	PART
ajst-20786	6	15	closely	closely	ADV
ajst-20786	6	16	related	relate	VERB
ajst-20786	6	17	to	to	ADP
ajst-20786	6	18	the	the	DET
ajst-20786	6	19	change	change	NOUN
ajst-20786	6	20	of	of	ADP
ajst-20786	6	21	models	model	NOUN
ajst-20786	6	22	.	.	PUNCT
ajst-20786	7	1	in	in	ADP
ajst-20786	7	2	general	general	ADJ
ajst-20786	7	3	,	,	PUNCT
ajst-20786	7	4	resnet-18	resnet-18	PROPN
ajst-20786	7	5	reached	reach	VERB
ajst-20786	7	6	the	the	DET
ajst-20786	7	7	highest	high	ADJ
ajst-20786	7	8	average	average	ADJ
ajst-20786	7	9	classification	classification	NOUN
ajst-20786	7	10	accuracy	accuracy	NOUN
ajst-20786	7	11	at	at	ADP
ajst-20786	7	12	approximate	approximate	ADJ
ajst-20786	7	13	80	80	NUM
ajst-20786	7	14	%	%	NOUN
ajst-20786	7	15	,	,	PUNCT
ajst-20786	7	16	while	while	SCONJ
ajst-20786	7	17	alexnet	alexnet	NOUN
ajst-20786	7	18	did	do	VERB
ajst-20786	7	19	best	well	ADV
ajst-20786	7	20	in	in	ADP
ajst-20786	7	21	finding	find	VERB
ajst-20786	7	22	hip	hip	NOUN
ajst-20786	7	23	-	-	PUNCT
ajst-20786	7	24	hop	hop	NOUN
ajst-20786	7	25	music	music	NOUN
ajst-20786	7	26	and	and	CCONJ
ajst-20786	7	27	googlenet	googlenet	NOUN
ajst-20786	7	28	had	have	VERB
ajst-20786	7	29	relatively	relatively	ADV
ajst-20786	7	30	less	less	ADJ
ajst-20786	7	31	difference	difference	NOUN
ajst-20786	7	32	in	in	ADP
ajst-20786	7	33	recognition	recognition	NOUN
ajst-20786	7	34	rates	rate	NOUN
ajst-20786	7	35	for	for	ADP
ajst-20786	7	36	every	every	DET
ajst-20786	7	37	genre	genre	NOUN
ajst-20786	7	38	.	.	PUNCT
ajst-20786	8	1	those	those	DET
ajst-20786	8	2	findings	finding	NOUN
ajst-20786	8	3	can	can	AUX
ajst-20786	8	4	serve	serve	VERB
ajst-20786	8	5	as	as	ADP
ajst-20786	8	6	a	a	DET
ajst-20786	8	7	reference	reference	NOUN
ajst-20786	8	8	in	in	ADP
ajst-20786	8	9	future	future	ADJ
ajst-20786	8	10	music	music	NOUN
ajst-20786	8	11	genre	genre	NOUN
ajst-20786	8	12	classification	classification	NOUN
ajst-20786	8	13	tasks	task	NOUN
ajst-20786	8	14	and	and	CCONJ
ajst-20786	8	15	personalized	personalized	ADJ
ajst-20786	8	16	music	music	NOUN
ajst-20786	8	17	recommendation	recommendation	NOUN
ajst-20786	8	18	based	base	VERB
ajst-20786	8	19	on	on	ADP
ajst-20786	8	20	big	big	ADJ
ajst-20786	8	21	data	datum	NOUN
ajst-20786	8	22	.	.	PUNCT
ajst-20786	9	1	keywords	keyword	NOUN
ajst-20786	9	2	:	:	PUNCT
ajst-20786	9	3	deep	deep	ADJ
ajst-20786	9	4	learning	learning	NOUN
ajst-20786	9	5	,	,	PUNCT
ajst-20786	9	6	music	music	NOUN
ajst-20786	9	7	genre	genre	NOUN
ajst-20786	9	8	classification	classification	NOUN
ajst-20786	9	9	,	,	PUNCT
ajst-20786	9	10	convolutional	convolutional	ADJ
ajst-20786	9	11	neural	neural	ADJ
ajst-20786	9	12	networks	network	NOUN
ajst-20786	9	13	.	.	PUNCT
ajst-20786	10	1	1	1	X
ajst-20786	10	2	.	.	X
ajst-20786	10	3	introduction	introduction	NOUN
ajst-20786	10	4	listening	listen	VERB
ajst-20786	10	5	to	to	ADP
ajst-20786	10	6	digital	digital	ADJ
ajst-20786	10	7	music	music	NOUN
ajst-20786	10	8	has	have	AUX
ajst-20786	10	9	already	already	ADV
ajst-20786	10	10	been	be	AUX
ajst-20786	10	11	one	one	NUM
ajst-20786	10	12	of	of	ADP
ajst-20786	10	13	the	the	DET
ajst-20786	10	14	common	common	ADJ
ajst-20786	10	15	leisure	leisure	NOUN
ajst-20786	10	16	pursuits	pursuit	NOUN
ajst-20786	10	17	for	for	ADP
ajst-20786	10	18	individuals	individual	NOUN
ajst-20786	10	19	.	.	PUNCT
ajst-20786	11	1	alongside	alongside	ADP
ajst-20786	11	2	the	the	DET
ajst-20786	11	3	advent	advent	NOUN
ajst-20786	11	4	of	of	ADP
ajst-20786	11	5	the	the	DET
ajst-20786	11	6	information	information	NOUN
ajst-20786	11	7	revolution	revolution	NOUN
ajst-20786	11	8	,	,	PUNCT
ajst-20786	11	9	people	people	NOUN
ajst-20786	11	10	nowadays	nowadays	ADV
ajst-20786	11	11	can	can	AUX
ajst-20786	11	12	access	access	VERB
ajst-20786	11	13	and	and	CCONJ
ajst-20786	11	14	download	download	NOUN
ajst-20786	11	15	music	music	NOUN
ajst-20786	11	16	online	online	ADV
ajst-20786	11	17	much	much	ADV
ajst-20786	11	18	more	more	ADV
ajst-20786	11	19	easily	easily	ADV
ajst-20786	11	20	than	than	ADP
ajst-20786	11	21	before	before	ADV
ajst-20786	11	22	.	.	PUNCT
ajst-20786	12	1	however	however	ADV
ajst-20786	12	2	,	,	PUNCT
ajst-20786	12	3	due	due	ADP
ajst-20786	12	4	to	to	ADP
ajst-20786	12	5	the	the	DET
ajst-20786	12	6	lack	lack	NOUN
ajst-20786	12	7	of	of	ADP
ajst-20786	12	8	standardized	standardized	ADJ
ajst-20786	12	9	metadata	metadata	NOUN
ajst-20786	12	10	information	information	NOUN
ajst-20786	12	11	for	for	ADP
ajst-20786	12	12	these	these	DET
ajst-20786	12	13	streaming	streaming	NOUN
ajst-20786	12	14	media	medium	NOUN
ajst-20786	12	15	,	,	PUNCT
ajst-20786	12	16	many	many	ADJ
ajst-20786	12	17	music	music	NOUN
ajst-20786	12	18	lacks	lack	VERB
ajst-20786	12	19	genre	genre	NOUN
ajst-20786	12	20	information	information	NOUN
ajst-20786	12	21	or	or	CCONJ
ajst-20786	12	22	contains	contain	VERB
ajst-20786	12	23	incorrect	incorrect	ADJ
ajst-20786	12	24	genre	genre	NOUN
ajst-20786	12	25	information	information	NOUN
ajst-20786	12	26	,	,	PUNCT
ajst-20786	12	27	which	which	PRON
ajst-20786	12	28	affects	affect	VERB
ajst-20786	12	29	the	the	DET
ajst-20786	12	30	effectiveness	effectiveness	NOUN
ajst-20786	12	31	of	of	ADP
ajst-20786	12	32	users	user	NOUN
ajst-20786	12	33	searching	search	VERB
ajst-20786	12	34	for	for	ADP
ajst-20786	12	35	songs	song	NOUN
ajst-20786	12	36	through	through	ADP
ajst-20786	12	37	genre	genre	NOUN
ajst-20786	12	38	keywords	keyword	NOUN
ajst-20786	12	39	and	and	CCONJ
ajst-20786	12	40	recommending	recommend	VERB
ajst-20786	12	41	similar	similar	ADJ
ajst-20786	12	42	songs	song	NOUN
ajst-20786	12	43	through	through	ADP
ajst-20786	12	44	big	big	ADJ
ajst-20786	12	45	data	datum	NOUN
ajst-20786	12	46	.	.	PUNCT
ajst-20786	13	1	automatic	automatic	ADJ
ajst-20786	13	2	music	music	NOUN
ajst-20786	13	3	genre	genre	NOUN
ajst-20786	13	4	classification	classification	NOUN
ajst-20786	13	5	(	(	PUNCT
ajst-20786	13	6	amgc	amgc	NOUN
ajst-20786	13	7	)	)	PUNCT
ajst-20786	13	8	has	have	AUX
ajst-20786	13	9	emerged	emerge	VERB
ajst-20786	13	10	as	as	ADP
ajst-20786	13	11	a	a	DET
ajst-20786	13	12	popular	popular	ADJ
ajst-20786	13	13	technology	technology	NOUN
ajst-20786	13	14	for	for	ADP
ajst-20786	13	15	mitigating	mitigate	VERB
ajst-20786	13	16	the	the	DET
ajst-20786	13	17	burden	burden	NOUN
ajst-20786	13	18	and	and	CCONJ
ajst-20786	13	19	expenses	expense	NOUN
ajst-20786	13	20	associated	associate	VERB
ajst-20786	13	21	with	with	ADP
ajst-20786	13	22	manually	manually	ADV
ajst-20786	13	23	categorizing	categorize	VERB
ajst-20786	13	24	a	a	DET
ajst-20786	13	25	vast	vast	ADJ
ajst-20786	13	26	volume	volume	NOUN
ajst-20786	13	27	of	of	ADP
ajst-20786	13	28	music	music	NOUN
ajst-20786	13	29	genres.[1	genres.[1	PRON
ajst-20786	13	30	]	]	PUNCT
ajst-20786	14	1	it	it	PRON
ajst-20786	14	2	was	be	AUX
ajst-20786	14	3	defined	define	VERB
ajst-20786	14	4	and	and	CCONJ
ajst-20786	14	5	named	name	VERB
ajst-20786	14	6	by	by	ADP
ajst-20786	14	7	tzanetakis	tzanetaki	NOUN
ajst-20786	14	8	and	and	CCONJ
ajst-20786	14	9	cook	cook	VERB
ajst-20786	14	10	,	,	PUNCT
ajst-20786	14	11	dating	date	VERB
ajst-20786	14	12	back	back	ADV
ajst-20786	14	13	to	to	ADP
ajst-20786	14	14	2002.[2	2002.[2	NUM
ajst-20786	14	15	]	]	PUNCT
ajst-20786	14	16	during	during	ADP
ajst-20786	14	17	that	that	DET
ajst-20786	14	18	period	period	NOUN
ajst-20786	14	19	,	,	PUNCT
ajst-20786	14	20	the	the	DET
ajst-20786	14	21	limited	limited	ADJ
ajst-20786	14	22	popularity	popularity	NOUN
ajst-20786	14	23	of	of	ADP
ajst-20786	14	24	deep	deep	ADJ
ajst-20786	14	25	learning	learning	NOUN
ajst-20786	14	26	resulted	result	VERB
ajst-20786	14	27	in	in	ADP
ajst-20786	14	28	the	the	DET
ajst-20786	14	29	phenomenon	phenomenon	NOUN
ajst-20786	14	30	which	which	PRON
ajst-20786	14	31	a	a	DET
ajst-20786	14	32	significant	significant	ADJ
ajst-20786	14	33	number	number	NOUN
ajst-20786	14	34	of	of	ADP
ajst-20786	14	35	researchers	researcher	NOUN
ajst-20786	14	36	opted	opt	VERB
ajst-20786	14	37	for	for	ADP
ajst-20786	14	38	machine	machine	NOUN
ajst-20786	14	39	learning	learn	VERB
ajst-20786	14	40	techniques	technique	NOUN
ajst-20786	14	41	to	to	PART
ajst-20786	14	42	classify	classify	VERB
ajst-20786	14	43	music	music	NOUN
ajst-20786	14	44	genres	genre	NOUN
ajst-20786	14	45	.	.	PUNCT
ajst-20786	15	1	for	for	ADP
ajst-20786	15	2	example	example	NOUN
ajst-20786	15	3	,	,	PUNCT
ajst-20786	15	4	kosina	kosina	PROPN
ajst-20786	15	5	utilized	utilize	VERB
ajst-20786	15	6	a	a	DET
ajst-20786	15	7	3	3	NUM
ajst-20786	15	8	-	-	PUNCT
ajst-20786	15	9	nn	nn	NOUN
ajst-20786	15	10	classifier	classifier	NOUN
ajst-20786	15	11	to	to	PART
ajst-20786	15	12	analyze	analyze	VERB
ajst-20786	15	13	music	music	NOUN
ajst-20786	15	14	features	feature	NOUN
ajst-20786	15	15	provided	provide	VERB
ajst-20786	15	16	by	by	ADP
ajst-20786	15	17	the	the	DET
ajst-20786	15	18	marsyas	marsyas	PROPN
ajst-20786	15	19	framework	framework	NOUN
ajst-20786	15	20	and	and	CCONJ
ajst-20786	15	21	achieved	achieve	VERB
ajst-20786	15	22	an	an	DET
ajst-20786	15	23	average	average	ADJ
ajst-20786	15	24	accuracy	accuracy	NOUN
ajst-20786	15	25	of	of	ADP
ajst-20786	15	26	88.35%.[3	88.35%.[3	NUM
ajst-20786	15	27	]	]	PUNCT
ajst-20786	15	28	although	although	SCONJ
ajst-20786	15	29	traditional	traditional	ADJ
ajst-20786	15	30	machine	machine	NOUN
ajst-20786	15	31	learning	learning	NOUN
ajst-20786	15	32	methods	method	NOUN
ajst-20786	15	33	have	have	AUX
ajst-20786	15	34	been	be	AUX
ajst-20786	15	35	applied	apply	VERB
ajst-20786	15	36	in	in	ADP
ajst-20786	15	37	amgc	amgc	NOUN
ajst-20786	15	38	tasks	task	NOUN
ajst-20786	15	39	already	already	ADV
ajst-20786	15	40	,	,	PUNCT
ajst-20786	15	41	they	they	PRON
ajst-20786	15	42	may	may	AUX
ajst-20786	15	43	not	not	PART
ajst-20786	15	44	be	be	AUX
ajst-20786	15	45	the	the	DET
ajst-20786	15	46	optimal	optimal	ADJ
ajst-20786	15	47	choice	choice	NOUN
ajst-20786	15	48	nowadays	nowadays	ADV
ajst-20786	15	49	.	.	PUNCT
ajst-20786	16	1	one	one	NUM
ajst-20786	16	2	main	main	ADJ
ajst-20786	16	3	reason	reason	NOUN
ajst-20786	16	4	is	be	AUX
ajst-20786	16	5	that	that	SCONJ
ajst-20786	16	6	those	those	DET
ajst-20786	16	7	kinds	kind	NOUN
ajst-20786	16	8	of	of	ADP
ajst-20786	16	9	technology	technology	NOUN
ajst-20786	16	10	require	require	VERB
ajst-20786	16	11	a	a	DET
ajst-20786	16	12	significant	significant	ADJ
ajst-20786	16	13	amount	amount	NOUN
ajst-20786	16	14	of	of	ADP
ajst-20786	16	15	human	human	ADJ
ajst-20786	16	16	workload	workload	NOUN
ajst-20786	16	17	to	to	PART
ajst-20786	16	18	select	select	VERB
ajst-20786	16	19	features	feature	NOUN
ajst-20786	16	20	of	of	ADP
ajst-20786	16	21	the	the	DET
ajst-20786	16	22	music	music	NOUN
ajst-20786	16	23	for	for	ADP
ajst-20786	16	24	classfiers	classfier	NOUN
ajst-20786	16	25	.	.	PUNCT
ajst-20786	17	1	in	in	ADP
ajst-20786	17	2	addition	addition	NOUN
ajst-20786	17	3	,	,	PUNCT
ajst-20786	17	4	the	the	DET
ajst-20786	17	5	enhancement	enhancement	NOUN
ajst-20786	17	6	of	of	ADP
ajst-20786	17	7	traditional	traditional	ADJ
ajst-20786	17	8	classifiers	classifier	NOUN
ajst-20786	17	9	only	only	ADV
ajst-20786	17	10	has	have	VERB
ajst-20786	17	11	a	a	DET
ajst-20786	17	12	somewhat	somewhat	ADV
ajst-20786	17	13	restricted	restricted	ADJ
ajst-20786	17	14	influence	influence	NOUN
ajst-20786	17	15	on	on	ADP
ajst-20786	17	16	the	the	DET
ajst-20786	17	17	outcome	outcome	NOUN
ajst-20786	17	18	.	.	PUNCT
ajst-20786	18	1	according	accord	VERB
ajst-20786	18	2	to	to	ADP
ajst-20786	18	3	a	a	DET
ajst-20786	18	4	study	study	NOUN
ajst-20786	18	5	conducted	conduct	VERB
ajst-20786	18	6	by	by	ADP
ajst-20786	18	7	carlos	carlos	PROPN
ajst-20786	18	8	n.	n.	PROPN
ajst-20786	18	9	silla	silla	PROPN
ajst-20786	18	10	jr	jr	PROPN
ajst-20786	18	11	.	.	PROPN
ajst-20786	18	12	,	,	PUNCT
ajst-20786	18	13	alessandro	alessandro	PROPN
ajst-20786	18	14	l.	l.	PROPN
ajst-20786	18	15	koerich	koerich	PROPN
ajst-20786	18	16	,	,	PUNCT
ajst-20786	18	17	and	and	CCONJ
ajst-20786	18	18	celso	celso	PROPN
ajst-20786	18	19	a.a	a.a	PROPN
ajst-20786	18	20	.	.	PROPN
ajst-20786	18	21	kaestner	kaestner	PROPN
ajst-20786	18	22	,	,	PUNCT
ajst-20786	18	23	the	the	DET
ajst-20786	18	24	accuracy	accuracy	NOUN
ajst-20786	18	25	of	of	ADP
ajst-20786	18	26	classifiers	classifier	NOUN
ajst-20786	18	27	such	such	ADJ
ajst-20786	18	28	as	as	ADP
ajst-20786	18	29	3	3	NUM
ajst-20786	18	30	-	-	SYM
ajst-20786	18	31	nn	nn	NOUN
ajst-20786	18	32	,	,	PUNCT
ajst-20786	18	33	oaa	oaa	ADJ
ajst-20786	18	34	,	,	PUNCT
ajst-20786	18	35	and	and	CCONJ
ajst-20786	18	36	fsrr	fsrr	PROPN
ajst-20786	18	37	is	be	AUX
ajst-20786	18	38	limited	limit	VERB
ajst-20786	18	39	to	to	ADP
ajst-20786	18	40	below	below	ADP
ajst-20786	18	41	70	70	NUM
ajst-20786	18	42	%	%	NOUN
ajst-20786	18	43	when	when	SCONJ
ajst-20786	18	44	using	use	VERB
ajst-20786	18	45	various	various	ADJ
ajst-20786	18	46	music	music	NOUN
ajst-20786	18	47	decomposition	decomposition	NOUN
ajst-20786	18	48	strategies.[1	strategies.[1	NOUN
ajst-20786	18	49	]	]	PUNCT
ajst-20786	18	50	with	with	ADP
ajst-20786	18	51	the	the	DET
ajst-20786	18	52	advent	advent	NOUN
ajst-20786	18	53	of	of	ADP
ajst-20786	18	54	several	several	ADJ
ajst-20786	18	55	models	model	NOUN
ajst-20786	18	56	designed	design	VERB
ajst-20786	18	57	based	base	VERB
ajst-20786	18	58	on	on	ADP
ajst-20786	18	59	the	the	DET
ajst-20786	18	60	lenet	lenet	NOUN
ajst-20786	18	61	model	model	NOUN
ajst-20786	18	62	,	,	PUNCT
ajst-20786	18	63	the	the	DET
ajst-20786	18	64	public	public	NOUN
ajst-20786	18	65	started	start	VERB
ajst-20786	18	66	to	to	PART
ajst-20786	18	67	introduce	introduce	VERB
ajst-20786	18	68	deep	deep	ADJ
ajst-20786	18	69	learning	learning	NOUN
ajst-20786	18	70	technology	technology	NOUN
ajst-20786	18	71	into	into	ADP
ajst-20786	18	72	amgc	amgc	NOUN
ajst-20786	18	73	tasks	task	NOUN
ajst-20786	18	74	.	.	PUNCT
ajst-20786	19	1	with	with	ADP
ajst-20786	19	2	certainty	certainty	PROPN
ajst-20786	19	3	,	,	PUNCT
ajst-20786	19	4	the	the	DET
ajst-20786	19	5	performance	performance	NOUN
ajst-20786	19	6	of	of	ADP
ajst-20786	19	7	deep	deep	ADJ
ajst-20786	19	8	learning	learning	NOUN
ajst-20786	19	9	was	be	AUX
ajst-20786	19	10	general	general	ADJ
ajst-20786	19	11	satisfying	satisfying	ADJ
ajst-20786	19	12	.	.	PUNCT
ajst-20786	20	1	in	in	ADP
ajst-20786	20	2	a	a	DET
ajst-20786	20	3	research	research	NOUN
ajst-20786	20	4	carried	carry	VERB
ajst-20786	20	5	out	out	ADP
ajst-20786	20	6	by	by	ADP
ajst-20786	20	7	yan	yan	PROPN
ajst-20786	20	8	jiacheng	jiacheng	PROPN
ajst-20786	20	9	,	,	PUNCT
ajst-20786	20	10	the	the	DET
ajst-20786	20	11	convolutional	convolutional	ADJ
ajst-20786	20	12	neural	neural	ADJ
ajst-20786	20	13	network	network	NOUN
ajst-20786	20	14	which	which	PRON
ajst-20786	20	15	was	be	AUX
ajst-20786	20	16	designed	design	VERB
ajst-20786	20	17	under	under	ADP
ajst-20786	20	18	the	the	DET
ajst-20786	20	19	structure	structure	NOUN
ajst-20786	20	20	of	of	ADP
ajst-20786	20	21	alexnet	alexnet	PROPN
ajst-20786	20	22	reached	reach	VERB
ajst-20786	20	23	an	an	DET
ajst-20786	20	24	average	average	ADJ
ajst-20786	20	25	accuracy	accuracy	NOUN
ajst-20786	20	26	rate	rate	NOUN
ajst-20786	20	27	at	at	ADP
ajst-20786	20	28	about	about	ADV
ajst-20786	20	29	87	87	NUM
ajst-20786	20	30	%	%	NOUN
ajst-20786	20	31	,	,	PUNCT
ajst-20786	20	32	and	and	CCONJ
ajst-20786	20	33	the	the	DET
ajst-20786	20	34	network	network	NOUN
ajst-20786	20	35	based	base	VERB
ajst-20786	20	36	on	on	ADP
ajst-20786	20	37	vgg16	vgg16	NOUN
ajst-20786	20	38	improved	improve	VERB
ajst-20786	20	39	accuracy	accuracy	NOUN
ajst-20786	20	40	to	to	ADP
ajst-20786	20	41	approximately	approximately	ADV
ajst-20786	20	42	91	91	NUM
ajst-20786	20	43	%	%	NOUN
ajst-20786	20	44	in	in	ADP
ajst-20786	20	45	classifying	classify	VERB
ajst-20786	20	46	the	the	DET
ajst-20786	20	47	music	music	NOUN
ajst-20786	20	48	dataset	dataset	VERB
ajst-20786	20	49	gtzan.[4	gtzan.[4	X
ajst-20786	20	50	]	]	PUNCT
ajst-20786	20	51	what	what	PRON
ajst-20786	20	52	’s	’	VERB
ajst-20786	20	53	more	more	ADJ
ajst-20786	20	54	,	,	PUNCT
ajst-20786	20	55	the	the	DET
ajst-20786	20	56	method	method	NOUN
ajst-20786	20	57	that	that	PRON
ajst-20786	20	58	combines	combine	VERB
ajst-20786	20	59	rglu	rglu	PROPN
ajst-20786	20	60	-	-	PUNCT
ajst-20786	20	61	se	se	X
ajst-20786	20	62	convolutional	convolutional	ADJ
ajst-20786	20	63	structure	structure	NOUN
ajst-20786	20	64	with	with	ADP
ajst-20786	20	65	bidirectional	bidirectional	ADJ
ajst-20786	20	66	long	long	ADJ
ajst-20786	20	67	short	short	ADJ
ajst-20786	20	68	-	-	PUNCT
ajst-20786	20	69	term	term	NOUN
ajst-20786	20	70	memory	memory	NOUN
ajst-20786	20	71	(	(	PUNCT
ajst-20786	20	72	blstm	blstm	NOUN
ajst-20786	20	73	)	)	PUNCT
ajst-20786	20	74	indicated	indicate	VERB
ajst-20786	20	75	the	the	DET
ajst-20786	20	76	accuracy	accuracy	NOUN
ajst-20786	20	77	to	to	ADP
ajst-20786	20	78	92.2	92.2	NUM
ajst-20786	20	79	%	%	NOUN
ajst-20786	20	80	about	about	ADP
ajst-20786	20	81	classifying	classify	VERB
ajst-20786	20	82	the	the	DET
ajst-20786	20	83	dataset	dataset	NOUN
ajst-20786	20	84	gtzan	gtzan	NOUN
ajst-20786	20	85	in	in	ADP
ajst-20786	20	86	the	the	DET
ajst-20786	20	87	context	context	NOUN
ajst-20786	20	88	of	of	ADP
ajst-20786	20	89	using	use	VERB
ajst-20786	20	90	attention	attention	NOUN
ajst-20786	20	91	mechanism	mechanism	NOUN
ajst-20786	20	92	for	for	ADP
ajst-20786	20	93	sequence	sequence	NOUN
ajst-20786	20	94	feature	feature	NOUN
ajst-20786	20	95	aggregation	aggregation	NOUN
ajst-20786	20	96	.	.	PUNCT
ajst-20786	21	1	[	[	X
ajst-20786	21	2	5	5	NUM
ajst-20786	21	3	]	]	PUNCT
ajst-20786	21	4	recently	recently	ADV
ajst-20786	21	5	,	,	PUNCT
ajst-20786	21	6	the	the	DET
ajst-20786	21	7	steps	step	NOUN
ajst-20786	21	8	of	of	ADP
ajst-20786	21	9	applying	apply	VERB
ajst-20786	21	10	convolutional	convolutional	ADJ
ajst-20786	21	11	neural	neural	ADJ
ajst-20786	21	12	network	network	NOUN
ajst-20786	21	13	to	to	PART
ajst-20786	21	14	classify	classify	VERB
ajst-20786	21	15	music	music	NOUN
ajst-20786	21	16	genres	genre	NOUN
ajst-20786	21	17	can	can	AUX
ajst-20786	21	18	be	be	AUX
ajst-20786	21	19	concluded	conclude	VERB
ajst-20786	21	20	generally	generally	ADV
ajst-20786	21	21	into	into	ADP
ajst-20786	21	22	three	three	NUM
ajst-20786	21	23	parts	part	NOUN
ajst-20786	21	24	:	:	PUNCT
ajst-20786	21	25	making	make	VERB
ajst-20786	21	26	mel	mel	PROPN
ajst-20786	21	27	spectrograms	spectrograms	PROPN
ajst-20786	21	28	,	,	PUNCT
ajst-20786	21	29	data	datum	NOUN
ajst-20786	21	30	augmentation	augmentation	NOUN
ajst-20786	21	31	and	and	CCONJ
ajst-20786	21	32	model	model	NOUN
ajst-20786	21	33	training	training	NOUN
ajst-20786	21	34	.	.	PUNCT
ajst-20786	22	1	2	2	X
ajst-20786	22	2	.	.	X
ajst-20786	22	3	preparation	preparation	NOUN
ajst-20786	22	4	2.1	2.1	NUM
ajst-20786	22	5	.	.	PUNCT
ajst-20786	23	1	mel	mel	PROPN
ajst-20786	23	2	spectrogram	spectrogram	PROPN
ajst-20786	23	3	mel	mel	PROPN
ajst-20786	23	4	scale	scale	NOUN
ajst-20786	23	5	is	be	AUX
ajst-20786	23	6	a	a	DET
ajst-20786	23	7	set	set	NOUN
ajst-20786	23	8	of	of	ADP
ajst-20786	23	9	psychoacoustic	psychoacoustic	ADJ
ajst-20786	23	10	pitch	pitch	NOUN
ajst-20786	23	11	units	unit	NOUN
ajst-20786	23	12	which	which	PRON
ajst-20786	23	13	makes	make	VERB
ajst-20786	23	14	people	people	NOUN
ajst-20786	23	15	feel	feel	VERB
ajst-20786	23	16	the	the	DET
ajst-20786	23	17	same	same	ADJ
ajst-20786	23	18	difference	difference	NOUN
ajst-20786	23	19	between	between	ADP
ajst-20786	23	20	two	two	NUM
ajst-20786	23	21	mel	mel	PROPN
ajst-20786	23	22	notes	note	NOUN
ajst-20786	23	23	adjacent	adjacent	ADJ
ajst-20786	23	24	to	to	ADP
ajst-20786	23	25	each	each	DET
ajst-20786	23	26	other	other	ADJ
ajst-20786	23	27	at	at	ADP
ajst-20786	23	28	the	the	DET
ajst-20786	23	29	same	same	ADJ
ajst-20786	23	30	volume	volume	NOUN
ajst-20786	23	31	.	.	PUNCT
ajst-20786	24	1	to	to	PART
ajst-20786	24	2	be	be	AUX
ajst-20786	24	3	more	more	ADV
ajst-20786	24	4	precise	precise	ADJ
ajst-20786	24	5	,	,	PUNCT
ajst-20786	24	6	it	it	PRON
ajst-20786	24	7	provides	provide	VERB
ajst-20786	24	8	a	a	DET
ajst-20786	24	9	linear	linear	ADJ
ajst-20786	24	10	scale	scale	NOUN
ajst-20786	24	11	for	for	ADP
ajst-20786	24	12	the	the	DET
ajst-20786	24	13	human	human	ADJ
ajst-20786	24	14	auditory	auditory	ADJ
ajst-20786	24	15	system	system	NOUN
ajst-20786	24	16	.	.	PUNCT
ajst-20786	25	1	according	accord	VERB
ajst-20786	25	2	to	to	ADP
ajst-20786	25	3	it	it	PRON
ajst-20786	25	4	,	,	PUNCT
ajst-20786	25	5	we	we	PRON
ajst-20786	25	6	can	can	AUX
ajst-20786	25	7	use	use	VERB
ajst-20786	25	8	a	a	DET
ajst-20786	25	9	spectrogram	spectrogram	NOUN
ajst-20786	25	10	as	as	ADP
ajst-20786	25	11	a	a	DET
ajst-20786	25	12	visual	visual	ADJ
ajst-20786	25	13	description	description	NOUN
ajst-20786	25	14	for	for	ADP
ajst-20786	25	15	the	the	DET
ajst-20786	25	16	models	model	NOUN
ajst-20786	25	17	to	to	PART
ajst-20786	25	18	learn	learn	VERB
ajst-20786	25	19	the	the	DET
ajst-20786	25	20	features	feature	NOUN
ajst-20786	25	21	.	.	PUNCT
ajst-20786	26	1	the	the	DET
ajst-20786	26	2	frequency	frequency	NOUN
ajst-20786	26	3	of	of	ADP
ajst-20786	26	4	mel	mel	PROPN
ajst-20786	26	5	scale(m	scale(m	PROPN
ajst-20786	26	6	)	)	PUNCT
ajst-20786	26	7	can	can	AUX
ajst-20786	26	8	be	be	AUX
ajst-20786	26	9	calculated	calculate	VERB
ajst-20786	26	10	by	by	ADP
ajst-20786	26	11	such	such	ADJ
ajst-20786	26	12	formula	formula	NOUN
ajst-20786	26	13	below	below	ADP
ajst-20786	26	14	,	,	PUNCT
ajst-20786	26	15	where	where	SCONJ
ajst-20786	26	16	f	f	PROPN
ajst-20786	26	17	represents	represent	VERB
ajst-20786	26	18	hertz	hertz	NOUN
ajst-20786	26	19	.	.	PUNCT
ajst-20786	27	1	2.2	2.2	NUM
ajst-20786	27	2	.	.	PUNCT
ajst-20786	28	1	the	the	DET
ajst-20786	28	2	dataset	dataset	VERB
ajst-20786	28	3	the	the	DET
ajst-20786	28	4	free	free	ADJ
ajst-20786	28	5	music	music	NOUN
ajst-20786	28	6	archive	archive	NOUN
ajst-20786	28	7	(	(	PUNCT
ajst-20786	28	8	fma	fma	NOUN
ajst-20786	28	9	)	)	PUNCT
ajst-20786	28	10	is	be	AUX
ajst-20786	28	11	an	an	DET
ajst-20786	28	12	open	open	ADJ
ajst-20786	28	13	dataset	dataset	NOUN
ajst-20786	28	14	that	that	PRON
ajst-20786	28	15	contains	contain	VERB
ajst-20786	28	16	a	a	DET
ajst-20786	28	17	wide	wide	ADJ
ajst-20786	28	18	range	range	NOUN
ajst-20786	28	19	of	of	ADP
ajst-20786	28	20	music	music	NOUN
ajst-20786	28	21	genres	genre	NOUN
ajst-20786	28	22	and	and	CCONJ
ajst-20786	28	23	accurate	accurate	ADJ
ajst-20786	28	24	music	music	NOUN
ajst-20786	28	25	metadata	metadata	NOUN
ajst-20786	28	26	.	.	PUNCT
ajst-20786	29	1	this	this	DET
ajst-20786	29	2	dataset	dataset	NOUN
ajst-20786	29	3	is	be	AUX
ajst-20786	29	4	valuable	valuable	ADJ
ajst-20786	29	5	for	for	ADP
ajst-20786	29	6	both	both	DET
ajst-20786	29	7	music	music	NOUN
ajst-20786	29	8	information	information	NOUN
ajst-20786	29	9	retrieval	retrieval	NOUN
ajst-20786	29	10	(	(	PUNCT
ajst-20786	29	11	mir	mir	PROPN
ajst-20786	29	12	)	)	PUNCT
ajst-20786	29	13	and	and	CCONJ
ajst-20786	29	14	automatic	automatic	ADJ
ajst-20786	29	15	music	music	NOUN
ajst-20786	29	16	genre	genre	NOUN
ajst-20786	29	17	classification	classification	NOUN
ajst-20786	29	18	(	(	PUNCT
ajst-20786	29	19	amgc	amgc	PROPN
ajst-20786	29	20	)	)	PUNCT
ajst-20786	29	21	tasks[6	tasks[6	PRON
ajst-20786	29	22	]	]	PUNCT
ajst-20786	29	23	.	.	PUNCT
ajst-20786	30	1	i	i	PRON
ajst-20786	30	2	utilized	utilize	VERB
ajst-20786	30	3	python	python	NOUN
ajst-20786	30	4	to	to	PART
ajst-20786	30	5	convert	convert	VERB
ajst-20786	30	6	music	music	NOUN
ajst-20786	30	7	clips	clip	NOUN
ajst-20786	30	8	from	from	ADP
ajst-20786	30	9	the	the	DET
ajst-20786	30	10	fma	fma	NOUN
ajst-20786	30	11	small	small	ADJ
ajst-20786	30	12	dataset	dataset	NOUN
ajst-20786	30	13	,	,	PUNCT
ajst-20786	30	14	which	which	PRON
ajst-20786	30	15	consists	consist	VERB
ajst-20786	30	16	of	of	ADP
ajst-20786	30	17	8,000	8,000	NUM
ajst-20786	30	18	music	music	NOUN
ajst-20786	30	19	pieces	piece	NOUN
ajst-20786	30	20	spanning	span	VERB
ajst-20786	30	21	eight	eight	NUM
ajst-20786	30	22	different	different	ADJ
ajst-20786	30	23	genres	genre	NOUN
ajst-20786	30	24	in	in	ADP
ajst-20786	30	25	the	the	DET
ajst-20786	30	26	free	free	ADJ
ajst-20786	30	27	music	music	NOUN
ajst-20786	30	28	archive	archive	NOUN
ajst-20786	30	29	(	(	PUNCT
ajst-20786	30	30	fma	fma	NOUN
ajst-20786	30	31	)	)	PUNCT
ajst-20786	30	32	,	,	PUNCT
ajst-20786	30	33	into	into	ADP
ajst-20786	30	34	mel	mel	PROPN
ajst-20786	30	35	spectrograms	spectrograms	PROPN
ajst-20786	30	36	.	.	PUNCT
ajst-20786	31	1	figure	figure	NOUN
ajst-20786	31	2	1	1	NUM
ajst-20786	31	3	presented	present	VERB
ajst-20786	31	4	below	below	ADV
ajst-20786	31	5	shows	show	VERB
ajst-20786	31	6	one	one	NUM
ajst-20786	31	7	of	of	ADP
ajst-20786	31	8	the	the	DET
ajst-20786	31	9	mel	mel	PROPN
ajst-20786	31	10	spectrograms	spectrograms	PROPN
ajst-20786	31	11	.	.	PUNCT
ajst-20786	32	1	75	75	NUM
ajst-20786	32	2	figure	figure	NOUN
ajst-20786	32	3	1	1	NUM
ajst-20786	32	4	.	.	PUNCT
ajst-20786	33	1	a	a	DET
ajst-20786	33	2	mel	mel	PROPN
ajst-20786	33	3	spectrogram	spectrogram	PROPN
ajst-20786	33	4	2.3	2.3	NUM
ajst-20786	33	5	.	.	PUNCT
ajst-20786	34	1	data	datum	NOUN
ajst-20786	34	2	augmentation	augmentation	NOUN
ajst-20786	34	3	data	datum	NOUN
ajst-20786	34	4	augmentation	augmentation	NOUN
ajst-20786	34	5	is	be	AUX
ajst-20786	34	6	a	a	DET
ajst-20786	34	7	technology	technology	NOUN
ajst-20786	34	8	that	that	PRON
ajst-20786	34	9	expands	expand	VERB
ajst-20786	34	10	the	the	DET
ajst-20786	34	11	dataset	dataset	NOUN
ajst-20786	34	12	and	and	CCONJ
ajst-20786	34	13	improves	improve	VERB
ajst-20786	34	14	the	the	DET
ajst-20786	34	15	generalization	generalization	NOUN
ajst-20786	34	16	and	and	CCONJ
ajst-20786	34	17	robustness	robustness	NOUN
ajst-20786	34	18	of	of	ADP
ajst-20786	34	19	the	the	DET
ajst-20786	34	20	model	model	NOUN
ajst-20786	34	21	.	.	PUNCT
ajst-20786	35	1	flipping	flip	VERB
ajst-20786	35	2	,	,	PUNCT
ajst-20786	35	3	rotating	rotating	NOUN
ajst-20786	35	4	,	,	PUNCT
ajst-20786	35	5	cutting	cutting	NOUN
ajst-20786	35	6	,	,	PUNCT
ajst-20786	35	7	scaling	scaling	NOUN
ajst-20786	35	8	and	and	CCONJ
ajst-20786	35	9	translation	translation	NOUN
ajst-20786	35	10	are	be	AUX
ajst-20786	35	11	several	several	ADJ
ajst-20786	35	12	common	common	ADJ
ajst-20786	35	13	steps	step	NOUN
ajst-20786	35	14	in	in	ADP
ajst-20786	35	15	augmenting	augment	VERB
ajst-20786	35	16	data	datum	NOUN
ajst-20786	35	17	.	.	PUNCT
ajst-20786	36	1	the	the	DET
ajst-20786	36	2	inventors	inventor	NOUN
ajst-20786	36	3	of	of	ADP
ajst-20786	36	4	alexnet	alexnet	NOUN
ajst-20786	36	5	,	,	PUNCT
ajst-20786	36	6	including	include	VERB
ajst-20786	36	7	alex	alex	PROPN
ajst-20786	36	8	krizhevsky	krizhevsky	PROPN
ajst-20786	36	9	,	,	PUNCT
ajst-20786	36	10	applied	apply	VERB
ajst-20786	36	11	two	two	NUM
ajst-20786	36	12	methods	method	NOUN
ajst-20786	36	13	in	in	ADP
ajst-20786	36	14	data	datum	NOUN
ajst-20786	36	15	augmentation	augmentation	NOUN
ajst-20786	36	16	.	.	PUNCT
ajst-20786	37	1	the	the	DET
ajst-20786	37	2	first	first	ADJ
ajst-20786	37	3	one	one	NOUN
ajst-20786	37	4	was	be	AUX
ajst-20786	37	5	extracting	extract	VERB
ajst-20786	37	6	patches	patch	NOUN
ajst-20786	37	7	(	(	PUNCT
ajst-20786	37	8	including	include	VERB
ajst-20786	37	9	their	their	PRON
ajst-20786	37	10	horizontal	horizontal	ADJ
ajst-20786	37	11	reflections	reflection	NOUN
ajst-20786	37	12	)	)	PUNCT
ajst-20786	37	13	from	from	ADP
ajst-20786	37	14	original	original	ADJ
ajst-20786	37	15	images	image	NOUN
ajst-20786	37	16	,	,	PUNCT
ajst-20786	37	17	and	and	CCONJ
ajst-20786	37	18	then	then	ADV
ajst-20786	37	19	training	train	VERB
ajst-20786	37	20	the	the	DET
ajst-20786	37	21	model	model	NOUN
ajst-20786	37	22	with	with	ADP
ajst-20786	37	23	those	those	DET
ajst-20786	37	24	patches	patch	NOUN
ajst-20786	37	25	.	.	PUNCT
ajst-20786	38	1	another	another	DET
ajst-20786	38	2	method	method	NOUN
ajst-20786	38	3	was	be	AUX
ajst-20786	38	4	performing	perform	VERB
ajst-20786	38	5	principal	principal	ADJ
ajst-20786	38	6	component	component	NOUN
ajst-20786	38	7	analysis	analysis	NOUN
ajst-20786	38	8	(	(	PUNCT
ajst-20786	38	9	pca	pca	NOUN
ajst-20786	38	10	)	)	PUNCT
ajst-20786	38	11	on	on	ADP
ajst-20786	38	12	the	the	DET
ajst-20786	38	13	rgb	rgb	PROPN
ajst-20786	38	14	pixel	pixel	PROPN
ajst-20786	38	15	values	value	NOUN
ajst-20786	38	16	of	of	ADP
ajst-20786	38	17	each	each	DET
ajst-20786	38	18	image	image	NOUN
ajst-20786	38	19	,	,	PUNCT
ajst-20786	38	20	which	which	PRON
ajst-20786	38	21	reduced	reduce	VERB
ajst-20786	38	22	the	the	DET
ajst-20786	38	23	top-1	top-1	NOUN
ajst-20786	38	24	error	error	NOUN
ajst-20786	38	25	rate	rate	NOUN
ajst-20786	38	26	by	by	ADP
ajst-20786	38	27	up	up	ADP
ajst-20786	38	28	to	to	ADP
ajst-20786	38	29	1%.[7	1%.[7	NUM
ajst-20786	38	30	]	]	PUNCT
ajst-20786	38	31	2.4	2.4	NUM
ajst-20786	38	32	.	.	PUNCT
ajst-20786	39	1	cnn	cnn	PROPN
ajst-20786	39	2	models	model	NOUN
ajst-20786	39	3	on	on	ADP
ajst-20786	39	4	the	the	DET
ajst-20786	39	5	basis	basis	NOUN
ajst-20786	39	6	of	of	ADP
ajst-20786	39	7	the	the	DET
ajst-20786	39	8	efforts	effort	NOUN
ajst-20786	39	9	of	of	ADP
ajst-20786	39	10	previous	previous	ADJ
ajst-20786	39	11	researchers	researcher	NOUN
ajst-20786	39	12	,	,	PUNCT
ajst-20786	39	13	the	the	DET
ajst-20786	39	14	performance	performance	NOUN
ajst-20786	39	15	of	of	ADP
ajst-20786	39	16	convolutional	convolutional	ADJ
ajst-20786	39	17	neural	neural	ADJ
ajst-20786	39	18	networks	network	NOUN
ajst-20786	39	19	on	on	ADP
ajst-20786	39	20	computer	computer	NOUN
ajst-20786	39	21	vision	vision	NOUN
ajst-20786	39	22	has	have	AUX
ajst-20786	39	23	been	be	AUX
ajst-20786	39	24	improved	improve	VERB
ajst-20786	39	25	dramatically	dramatically	ADV
ajst-20786	39	26	,	,	PUNCT
ajst-20786	39	27	with	with	ADP
ajst-20786	39	28	technology	technology	NOUN
ajst-20786	39	29	ranging	range	VERB
ajst-20786	39	30	from	from	ADP
ajst-20786	39	31	grouping	group	VERB
ajst-20786	39	32	local	local	ADJ
ajst-20786	39	33	connections	connection	NOUN
ajst-20786	39	34	among	among	ADP
ajst-20786	39	35	pixels	pixel	NOUN
ajst-20786	39	36	to	to	ADP
ajst-20786	39	37	features	feature	NOUN
ajst-20786	39	38	sharing	share	VERB
ajst-20786	39	39	.	.	PUNCT
ajst-20786	40	1	in	in	ADP
ajst-20786	40	2	1998	1998	NUM
ajst-20786	40	3	,	,	PUNCT
ajst-20786	40	4	a	a	DET
ajst-20786	40	5	group	group	NOUN
ajst-20786	40	6	of	of	ADP
ajst-20786	40	7	researchers	researcher	NOUN
ajst-20786	40	8	including	include	VERB
ajst-20786	40	9	lecun	lecun	ADV
ajst-20786	40	10	published	publish	VERB
ajst-20786	40	11	a	a	DET
ajst-20786	40	12	convolutional	convolutional	ADJ
ajst-20786	40	13	neural	neural	ADJ
ajst-20786	40	14	model	model	NOUN
ajst-20786	40	15	lenet-5	lenet-5	PROPN
ajst-20786	40	16	,	,	PUNCT
ajst-20786	40	17	which	which	PRON
ajst-20786	40	18	has	have	AUX
ajst-20786	40	19	become	become	VERB
ajst-20786	40	20	a	a	DET
ajst-20786	40	21	source	source	NOUN
ajst-20786	40	22	of	of	ADP
ajst-20786	40	23	inspiration	inspiration	NOUN
ajst-20786	40	24	for	for	ADP
ajst-20786	40	25	designing	design	VERB
ajst-20786	40	26	new	new	ADJ
ajst-20786	40	27	model	model	NOUN
ajst-20786	40	28	structures	structure	NOUN
ajst-20786	40	29	.	.	PUNCT
ajst-20786	41	1	although	although	SCONJ
ajst-20786	41	2	lenet-5	lenet-5	PRON
ajst-20786	41	3	had	have	AUX
ajst-20786	41	4	been	be	AUX
ajst-20786	41	5	a	a	DET
ajst-20786	41	6	milestone	milestone	NOUN
ajst-20786	41	7	in	in	ADP
ajst-20786	41	8	deep	deep	ADJ
ajst-20786	41	9	learning	learning	NOUN
ajst-20786	41	10	,	,	PUNCT
ajst-20786	41	11	it	it	PRON
ajst-20786	41	12	still	still	ADV
ajst-20786	41	13	has	have	VERB
ajst-20786	41	14	several	several	ADJ
ajst-20786	41	15	shortcomings	shortcoming	NOUN
ajst-20786	41	16	,	,	PUNCT
ajst-20786	41	17	like	like	ADP
ajst-20786	41	18	the	the	DET
ajst-20786	41	19	excessive	excessive	ADJ
ajst-20786	41	20	data	datum	NOUN
ajst-20786	41	21	dependence	dependence	NOUN
ajst-20786	41	22	.	.	PUNCT
ajst-20786	42	1	from	from	ADP
ajst-20786	42	2	2012	2012	NUM
ajst-20786	42	3	to	to	ADP
ajst-20786	42	4	2023	2023	NUM
ajst-20786	42	5	,	,	PUNCT
ajst-20786	42	6	the	the	DET
ajst-20786	42	7	establishment	establishment	NOUN
ajst-20786	42	8	of	of	ADP
ajst-20786	42	9	more	more	ADJ
ajst-20786	42	10	databases	database	NOUN
ajst-20786	42	11	and	and	CCONJ
ajst-20786	42	12	modifications	modification	NOUN
ajst-20786	42	13	to	to	ADP
ajst-20786	42	14	neural	neural	ADJ
ajst-20786	42	15	network	network	NOUN
ajst-20786	42	16	architecture	architecture	NOUN
ajst-20786	42	17	have	have	AUX
ajst-20786	42	18	led	lead	VERB
ajst-20786	42	19	to	to	ADP
ajst-20786	42	20	the	the	DET
ajst-20786	42	21	proposal	proposal	NOUN
ajst-20786	42	22	of	of	ADP
ajst-20786	42	23	many	many	ADJ
ajst-20786	42	24	famous	famous	ADJ
ajst-20786	42	25	convolutional	convolutional	ADJ
ajst-20786	42	26	neural	neural	ADJ
ajst-20786	42	27	network	network	NOUN
ajst-20786	42	28	models.[8	models.[8	NOUN
ajst-20786	42	29	]	]	PUNCT
ajst-20786	42	30	all	all	DET
ajst-20786	42	31	models	model	NOUN
ajst-20786	42	32	this	this	DET
ajst-20786	42	33	research	research	NOUN
ajst-20786	42	34	includes	include	VERB
ajst-20786	42	35	are	be	AUX
ajst-20786	42	36	derived	derive	VERB
ajst-20786	42	37	from	from	ADP
ajst-20786	42	38	lenet-5	lenet-5	PUNCT
ajst-20786	42	39	and	and	CCONJ
ajst-20786	42	40	are	be	AUX
ajst-20786	42	41	published	publish	VERB
ajst-20786	42	42	in	in	ADP
ajst-20786	42	43	chronological	chronological	ADJ
ajst-20786	42	44	order	order	NOUN
ajst-20786	42	45	.	.	PUNCT
ajst-20786	43	1	each	each	PRON
ajst-20786	43	2	of	of	ADP
ajst-20786	43	3	them	they	PRON
ajst-20786	43	4	inherited	inherit	VERB
ajst-20786	43	5	and	and	CCONJ
ajst-20786	43	6	updated	update	VERB
ajst-20786	43	7	the	the	DET
ajst-20786	43	8	previous	previous	ADJ
ajst-20786	43	9	model	model	NOUN
ajst-20786	43	10	in	in	ADP
ajst-20786	43	11	some	some	DET
ajst-20786	43	12	specific	specific	ADJ
ajst-20786	43	13	regions	region	NOUN
ajst-20786	43	14	.	.	PUNCT
ajst-20786	44	1	thus	thus	ADV
ajst-20786	44	2	they	they	PRON
ajst-20786	44	3	shared	share	VERB
ajst-20786	44	4	both	both	DET
ajst-20786	44	5	similarities	similarity	NOUN
ajst-20786	44	6	and	and	CCONJ
ajst-20786	44	7	differences	difference	NOUN
ajst-20786	44	8	with	with	ADP
ajst-20786	44	9	each	each	DET
ajst-20786	44	10	other	other	ADJ
ajst-20786	44	11	.	.	PUNCT
ajst-20786	45	1	table	table	NOUN
ajst-20786	45	2	1	1	NUM
ajst-20786	45	3	illustrated	illustrate	VERB
ajst-20786	45	4	their	their	PRON
ajst-20786	45	5	main	main	ADJ
ajst-20786	45	6	properties	property	NOUN
ajst-20786	45	7	,	,	PUNCT
ajst-20786	45	8	which	which	PRON
ajst-20786	45	9	can	can	AUX
ajst-20786	45	10	serve	serve	VERB
ajst-20786	45	11	as	as	ADP
ajst-20786	45	12	a	a	DET
ajst-20786	45	13	reference	reference	NOUN
ajst-20786	45	14	for	for	ADP
ajst-20786	45	15	subsequent	subsequent	ADJ
ajst-20786	45	16	analyses	analysis	NOUN
ajst-20786	45	17	.	.	PUNCT
ajst-20786	46	1	[	[	X
ajst-20786	46	2	9	9	NUM
ajst-20786	46	3	-	-	SYM
ajst-20786	46	4	11	11	NUM
ajst-20786	46	5	]	]	PUNCT
ajst-20786	46	6	table	table	NOUN
ajst-20786	46	7	1	1	NUM
ajst-20786	46	8	.	.	PUNCT
ajst-20786	46	9	main	main	ADJ
ajst-20786	46	10	properties	property	NOUN
ajst-20786	46	11	about	about	ADP
ajst-20786	46	12	alexnet	alexnet	ADJ
ajst-20786	46	13	,	,	PUNCT
ajst-20786	46	14	googlenet	googlenet	NOUN
ajst-20786	46	15	and	and	CCONJ
ajst-20786	46	16	resnet-18	resnet-18	PROPN
ajst-20786	46	17	model	model	NOUN
ajst-20786	46	18	name	name	NOUN
ajst-20786	46	19	alexnet	alexnet	PROPN
ajst-20786	46	20	googlenet	googlenet	PROPN
ajst-20786	46	21	resnet-18	resnet-18	PROPN
ajst-20786	46	22	year	year	NOUN
ajst-20786	46	23	published	publish	VERB
ajst-20786	46	24	2012	2012	NUM
ajst-20786	46	25	2014	2014	NUM
ajst-20786	46	26	2015	2015	NUM
ajst-20786	46	27	key	key	ADJ
ajst-20786	46	28	innovation	innovation	NOUN
ajst-20786	46	29	demonstrated	demonstrate	VERB
ajst-20786	46	30	the	the	DET
ajst-20786	46	31	potential	potential	NOUN
ajst-20786	46	32	of	of	ADP
ajst-20786	46	33	deep	deep	ADJ
ajst-20786	46	34	learning	learning	NOUN
ajst-20786	46	35	in	in	ADP
ajst-20786	46	36	image	image	NOUN
ajst-20786	46	37	recognition	recognition	NOUN
ajst-20786	46	38	introduced	introduce	VERB
ajst-20786	46	39	the	the	DET
ajst-20786	46	40	inception	inception	NOUN
ajst-20786	46	41	module	module	NOUN
ajst-20786	46	42	to	to	PART
ajst-20786	46	43	improve	improve	VERB
ajst-20786	46	44	computational	computational	ADJ
ajst-20786	46	45	efficiency	efficiency	NOUN
ajst-20786	46	46	introduced	introduce	VERB
ajst-20786	46	47	residual	residual	ADJ
ajst-20786	46	48	connections	connection	NOUN
ajst-20786	46	49	to	to	PART
ajst-20786	46	50	address	address	VERB
ajst-20786	46	51	the	the	DET
ajst-20786	46	52	problem	problem	NOUN
ajst-20786	46	53	of	of	ADP
ajst-20786	46	54	gradient	gradient	ADJ
ajst-20786	46	55	vanishing	vanishing	NOUN
ajst-20786	46	56	in	in	ADP
ajst-20786	46	57	deep	deep	ADJ
ajst-20786	46	58	neural	neural	ADJ
ajst-20786	46	59	networks	network	NOUN
ajst-20786	46	60	number	number	NOUN
ajst-20786	46	61	of	of	ADP
ajst-20786	46	62	parameters	parameter	NOUN
ajst-20786	46	63	relatively	relatively	ADV
ajst-20786	46	64	high	high	ADV
ajst-20786	46	65	lower	lower	ADV
ajst-20786	46	66	compared	compare	VERB
ajst-20786	46	67	to	to	ADP
ajst-20786	46	68	alexnet	alexnet	ADV
ajst-20786	46	69	lower	lower	ADV
ajst-20786	46	70	compared	compare	VERB
ajst-20786	46	71	to	to	ADP
ajst-20786	46	72	alexnet	alexnet	ADJ
ajst-20786	46	73	and	and	CCONJ
ajst-20786	46	74	googlenet	googlenet	NOUN
ajst-20786	46	75	advantages	advantage	NOUN
ajst-20786	46	76	drove	drive	VERB
ajst-20786	46	77	the	the	DET
ajst-20786	46	78	advancement	advancement	NOUN
ajst-20786	46	79	of	of	ADP
ajst-20786	46	80	deep	deep	ADJ
ajst-20786	46	81	learning	learning	NOUN
ajst-20786	46	82	in	in	ADP
ajst-20786	46	83	computer	computer	NOUN
ajst-20786	46	84	vision	vision	NOUN
ajst-20786	46	85	improved	improve	VERB
ajst-20786	46	86	computational	computational	ADJ
ajst-20786	46	87	efficiency	efficiency	NOUN
ajst-20786	46	88	and	and	CCONJ
ajst-20786	46	89	accuracy	accuracy	NOUN
ajst-20786	46	90	(	(	PUNCT
ajst-20786	46	91	through	through	ADP
ajst-20786	46	92	the	the	DET
ajst-20786	46	93	inception	inception	NOUN
ajst-20786	46	94	module	module	NOUN
ajst-20786	46	95	)	)	PUNCT
ajst-20786	46	96	alleviated	alleviate	VERB
ajst-20786	46	97	the	the	DET
ajst-20786	46	98	gradient	gradient	NOUN
ajst-20786	46	99	vanishing	vanishing	NOUN
ajst-20786	46	100	problem	problem	NOUN
ajst-20786	46	101	and	and	CCONJ
ajst-20786	46	102	enabled	enable	VERB
ajst-20786	46	103	deeper	deep	ADJ
ajst-20786	46	104	network	network	NOUN
ajst-20786	46	105	architectures	architecture	NOUN
ajst-20786	46	106	disadvantages	disadvantage	VERB
ajst-20786	46	107	a	a	DET
ajst-20786	46	108	relatively	relatively	ADV
ajst-20786	46	109	high	high	ADJ
ajst-20786	46	110	number	number	NOUN
ajst-20786	46	111	of	of	ADP
ajst-20786	46	112	parameters	parameter	NOUN
ajst-20786	46	113	,	,	PUNCT
ajst-20786	46	114	which	which	PRON
ajst-20786	46	115	can	can	AUX
ajst-20786	46	116	lead	lead	VERB
ajst-20786	46	117	to	to	ADP
ajst-20786	46	118	overfitting	overfitting	NOUN
ajst-20786	46	119	may	may	AUX
ajst-20786	46	120	not	not	PART
ajst-20786	46	121	be	be	AUX
ajst-20786	46	122	as	as	ADV
ajst-20786	46	123	accurate	accurate	ADJ
ajst-20786	46	124	as	as	ADP
ajst-20786	46	125	other	other	ADJ
ajst-20786	46	126	models	model	NOUN
ajst-20786	46	127	for	for	ADP
ajst-20786	46	128	certain	certain	ADJ
ajst-20786	46	129	tasks	task	NOUN
ajst-20786	46	130	may	may	AUX
ajst-20786	46	131	not	not	PART
ajst-20786	46	132	be	be	AUX
ajst-20786	46	133	as	as	ADV
ajst-20786	46	134	accurate	accurate	ADJ
ajst-20786	46	135	as	as	ADP
ajst-20786	46	136	other	other	ADJ
ajst-20786	46	137	models	model	NOUN
ajst-20786	46	138	for	for	ADP
ajst-20786	46	139	certain	certain	ADJ
ajst-20786	46	140	tasks	task	NOUN
ajst-20786	46	141	3	3	NUM
ajst-20786	46	142	.	.	PUNCT
ajst-20786	46	143	experiment	experiment	NOUN
ajst-20786	46	144	and	and	CCONJ
ajst-20786	46	145	results	result	NOUN
ajst-20786	46	146	using	use	VERB
ajst-20786	46	147	a	a	DET
ajst-20786	46	148	single	single	ADJ
ajst-20786	46	149	processor	processor	NOUN
ajst-20786	46	150	,	,	PUNCT
ajst-20786	46	151	the	the	DET
ajst-20786	46	152	intel	intel	PROPN
ajst-20786	46	153	core	core	PROPN
ajst-20786	46	154	i5	i5	NOUN
ajst-20786	46	155	-	-	PUNCT
ajst-20786	46	156	12500h	12500h	NUM
ajst-20786	46	157	,	,	PUNCT
ajst-20786	46	158	the	the	DET
ajst-20786	46	159	experiment	experiment	NOUN
ajst-20786	46	160	was	be	AUX
ajst-20786	46	161	conducted	conduct	VERB
ajst-20786	46	162	based	base	VERB
ajst-20786	46	163	on	on	ADP
ajst-20786	46	164	the	the	DET
ajst-20786	46	165	python	python	NOUN
ajst-20786	46	166	3.9	3.9	NUM
ajst-20786	46	167	environment	environment	NOUN
ajst-20786	46	168	and	and	CCONJ
ajst-20786	46	169	various	various	ADJ
ajst-20786	46	170	third	third	ADJ
ajst-20786	46	171	-	-	PUNCT
ajst-20786	46	172	party	party	NOUN
ajst-20786	46	173	libraries	library	NOUN
ajst-20786	46	174	,	,	PUNCT
ajst-20786	46	175	including	include	VERB
ajst-20786	46	176	numpy	numpy	NOUN
ajst-20786	46	177	and	and	CCONJ
ajst-20786	46	178	torch	torch	NOUN
ajst-20786	46	179	.	.	PUNCT
ajst-20786	47	1	a	a	DET
ajst-20786	47	2	total	total	NOUN
ajst-20786	47	3	of	of	ADP
ajst-20786	47	4	8,000	8,000	NUM
ajst-20786	47	5	music	music	NOUN
ajst-20786	47	6	clips	clip	NOUN
ajst-20786	47	7	from	from	ADP
ajst-20786	47	8	the	the	DET
ajst-20786	47	9	fmasmall	fmasmall	PROPN
ajst-20786	47	10	database	database	NOUN
ajst-20786	47	11	were	be	AUX
ajst-20786	47	12	transformed	transform	VERB
ajst-20786	47	13	into	into	ADP
ajst-20786	47	14	mel	mel	PROPN
ajst-20786	47	15	spectrograms	spectrogram	NOUN
ajst-20786	47	16	and	and	CCONJ
ajst-20786	47	17	resized	resize	VERB
ajst-20786	47	18	to	to	ADP
ajst-20786	47	19	the	the	DET
ajst-20786	47	20	appropriate	appropriate	ADJ
ajst-20786	47	21	input	input	NOUN
ajst-20786	47	22	size	size	NOUN
ajst-20786	47	23	.	.	PUNCT
ajst-20786	48	1	and	and	CCONJ
ajst-20786	48	2	the	the	DET
ajst-20786	48	3	results	result	NOUN
ajst-20786	48	4	of	of	ADP
ajst-20786	48	5	alexnet	alexnet	NOUN
ajst-20786	48	6	,	,	PUNCT
ajst-20786	48	7	googlenet	googlenet	NOUN
ajst-20786	48	8	and	and	CCONJ
ajst-20786	48	9	resnet-18	resnet-18	PROPN
ajst-20786	48	10	were	be	AUX
ajst-20786	48	11	illustrated	illustrate	VERB
ajst-20786	48	12	in	in	ADP
ajst-20786	48	13	figure	figure	NOUN
ajst-20786	48	14	2	2	NUM
ajst-20786	48	15	.	.	NUM
ajst-20786	48	16	76	76	NUM
ajst-20786	48	17	(	(	PUNCT
ajst-20786	48	18	a	a	NOUN
ajst-20786	48	19	)	)	PUNCT
ajst-20786	48	20	(	(	PUNCT
ajst-20786	48	21	b	b	X
ajst-20786	48	22	)	)	PUNCT
ajst-20786	48	23	(	(	PUNCT
ajst-20786	48	24	c	c	X
ajst-20786	48	25	)	)	PUNCT
ajst-20786	48	26	figure	figure	NOUN
ajst-20786	48	27	2	2	NUM
ajst-20786	48	28	.	.	PUNCT
ajst-20786	49	1	the	the	DET
ajst-20786	49	2	loss	loss	NOUN
ajst-20786	49	3	and	and	CCONJ
ajst-20786	49	4	accuracy	accuracy	NOUN
ajst-20786	49	5	of	of	ADP
ajst-20786	49	6	each	each	DET
ajst-20786	49	7	model	model	NOUN
ajst-20786	49	8	(	(	PUNCT
ajst-20786	49	9	alexnet	alexnet	ADJ
ajst-20786	49	10	,	,	PUNCT
ajst-20786	49	11	googlenet	googlenet	NOUN
ajst-20786	49	12	and	and	CCONJ
ajst-20786	49	13	resnet-18	resnet-18	PROPN
ajst-20786	49	14	from	from	ADP
ajst-20786	49	15	left	leave	VERB
ajst-20786	49	16	to	to	ADP
ajst-20786	49	17	right	right	ADJ
ajst-20786	49	18	)	)	PUNCT
ajst-20786	50	1	it	it	PRON
ajst-20786	50	2	turned	turn	VERB
ajst-20786	50	3	out	out	ADP
ajst-20786	50	4	that	that	SCONJ
ajst-20786	50	5	the	the	DET
ajst-20786	50	6	accuracy	accuracy	NOUN
ajst-20786	50	7	of	of	ADP
ajst-20786	50	8	alexnet	alexnet	NOUN
ajst-20786	50	9	was	be	AUX
ajst-20786	50	10	only	only	ADV
ajst-20786	50	11	around	around	ADP
ajst-20786	50	12	25	25	NUM
ajst-20786	50	13	%	%	NOUN
ajst-20786	50	14	at	at	ADP
ajst-20786	50	15	the	the	DET
ajst-20786	50	16	beginning	beginning	NOUN
ajst-20786	50	17	,	,	PUNCT
ajst-20786	50	18	but	but	CCONJ
ajst-20786	50	19	after	after	ADP
ajst-20786	50	20	the	the	DET
ajst-20786	50	21	sixth	sixth	ADJ
ajst-20786	50	22	epoch	epoch	NOUN
ajst-20786	50	23	,	,	PUNCT
ajst-20786	50	24	it	it	PRON
ajst-20786	50	25	significantly	significantly	ADV
ajst-20786	50	26	improved	improve	VERB
ajst-20786	50	27	to	to	ADP
ajst-20786	50	28	around	around	ADV
ajst-20786	50	29	40	40	NUM
ajst-20786	50	30	%	%	NOUN
ajst-20786	50	31	.	.	PUNCT
ajst-20786	51	1	however	however	ADV
ajst-20786	51	2	,	,	PUNCT
ajst-20786	51	3	the	the	DET
ajst-20786	51	4	effect	effect	NOUN
ajst-20786	51	5	of	of	ADP
ajst-20786	51	6	more	more	ADJ
ajst-20786	51	7	epochs	epoch	NOUN
ajst-20786	51	8	on	on	ADP
ajst-20786	51	9	improving	improve	VERB
ajst-20786	51	10	accuracy	accuracy	NOUN
ajst-20786	51	11	was	be	AUX
ajst-20786	51	12	not	not	PART
ajst-20786	51	13	very	very	ADV
ajst-20786	51	14	significant	significant	ADJ
ajst-20786	51	15	,	,	PUNCT
ajst-20786	51	16	as	as	SCONJ
ajst-20786	51	17	can	can	AUX
ajst-20786	51	18	be	be	AUX
ajst-20786	51	19	seen	see	VERB
ajst-20786	51	20	from	from	ADP
ajst-20786	51	21	figure	figure	NOUN
ajst-20786	51	22	2(a	2(a	NUM
ajst-20786	51	23	)	)	PUNCT
ajst-20786	51	24	that	that	SCONJ
ajst-20786	51	25	it	it	PRON
ajst-20786	51	26	fluctuated	fluctuate	VERB
ajst-20786	51	27	between	between	ADP
ajst-20786	51	28	50	50	NUM
ajst-20786	51	29	%	%	NOUN
ajst-20786	51	30	and	and	CCONJ
ajst-20786	51	31	60	60	NUM
ajst-20786	51	32	%	%	NOUN
ajst-20786	51	33	.	.	PUNCT
ajst-20786	52	1	when	when	SCONJ
ajst-20786	52	2	it	it	PRON
ajst-20786	52	3	came	come	VERB
ajst-20786	52	4	to	to	ADP
ajst-20786	52	5	googlenet	googlenet	NOUN
ajst-20786	52	6	,	,	PUNCT
ajst-20786	52	7	the	the	DET
ajst-20786	52	8	accuracy	accuracy	NOUN
ajst-20786	52	9	in	in	ADP
ajst-20786	52	10	the	the	DET
ajst-20786	52	11	first	first	ADJ
ajst-20786	52	12	epoch	epoch	NOUN
ajst-20786	52	13	was	be	AUX
ajst-20786	52	14	approximately	approximately	ADV
ajst-20786	52	15	30	30	NUM
ajst-20786	52	16	%	%	NOUN
ajst-20786	52	17	and	and	CCONJ
ajst-20786	52	18	kept	keep	VERB
ajst-20786	52	19	climbing	climb	VERB
ajst-20786	52	20	up	up	ADP
ajst-20786	52	21	until	until	ADP
ajst-20786	52	22	the	the	DET
ajst-20786	52	23	10th	10th	ADJ
ajst-20786	52	24	epoch	epoch	NOUN
ajst-20786	52	25	.	.	PUNCT
ajst-20786	53	1	in	in	ADP
ajst-20786	53	2	comparison	comparison	NOUN
ajst-20786	53	3	with	with	ADP
ajst-20786	53	4	alexnet	alexnet	NOUN
ajst-20786	53	5	,	,	PUNCT
ajst-20786	53	6	the	the	DET
ajst-20786	53	7	accuracy	accuracy	NOUN
ajst-20786	53	8	of	of	ADP
ajst-20786	53	9	googlenet	googlenet	NOUN
ajst-20786	53	10	was	be	AUX
ajst-20786	53	11	also	also	ADV
ajst-20786	53	12	fluctuating	fluctuate	VERB
ajst-20786	53	13	from	from	ADP
ajst-20786	53	14	55	55	NUM
ajst-20786	53	15	%	%	NOUN
ajst-20786	53	16	to	to	PART
ajst-20786	53	17	60	60	NUM
ajst-20786	53	18	%	%	NOUN
ajst-20786	53	19	.	.	PUNCT
ajst-20786	54	1	figure	figure	NOUN
ajst-20786	54	2	2(b	2(b	NUM
ajst-20786	54	3	)	)	PUNCT
ajst-20786	54	4	illustrated	illustrate	VERB
ajst-20786	54	5	the	the	DET
ajst-20786	54	6	accuracy	accuracy	NOUN
ajst-20786	54	7	and	and	CCONJ
ajst-20786	54	8	loss	loss	NOUN
ajst-20786	54	9	of	of	ADP
ajst-20786	54	10	googlenet	googlenet	NOUN
ajst-20786	54	11	during	during	ADP
ajst-20786	54	12	50	50	NUM
ajst-20786	54	13	epochs	epoch	NOUN
ajst-20786	54	14	.	.	PUNCT
ajst-20786	55	1	figure	figure	NOUN
ajst-20786	55	2	2(c	2(c	NUM
ajst-20786	55	3	)	)	PUNCT
ajst-20786	55	4	shows	show	VERB
ajst-20786	55	5	that	that	SCONJ
ajst-20786	55	6	the	the	DET
ajst-20786	55	7	accuracy	accuracy	NOUN
ajst-20786	55	8	of	of	ADP
ajst-20786	55	9	resnet-18	resnet-18	PROPN
ajst-20786	55	10	maintained	maintain	VERB
ajst-20786	55	11	the	the	DET
ajst-20786	55	12	upward	upward	ADJ
ajst-20786	55	13	trend	trend	NOUN
ajst-20786	55	14	during	during	ADP
ajst-20786	55	15	50	50	NUM
ajst-20786	55	16	epochs	epoch	NOUN
ajst-20786	55	17	.	.	PUNCT
ajst-20786	56	1	in	in	ADP
ajst-20786	56	2	the	the	DET
ajst-20786	56	3	first	first	ADJ
ajst-20786	56	4	25	25	NUM
ajst-20786	56	5	epochs	epoch	NOUN
ajst-20786	56	6	,	,	PUNCT
ajst-20786	56	7	the	the	DET
ajst-20786	56	8	accuracy	accuracy	NOUN
ajst-20786	56	9	ascended	ascend	VERB
ajst-20786	56	10	from	from	ADP
ajst-20786	56	11	30	30	NUM
ajst-20786	56	12	%	%	NOUN
ajst-20786	56	13	to	to	PART
ajst-20786	56	14	65	65	NUM
ajst-20786	56	15	%	%	NOUN
ajst-20786	56	16	.	.	PUNCT
ajst-20786	57	1	after	after	ADP
ajst-20786	57	2	that	that	PRON
ajst-20786	57	3	,	,	PUNCT
ajst-20786	57	4	its	its	PRON
ajst-20786	57	5	value	value	NOUN
ajst-20786	57	6	maintained	maintain	VERB
ajst-20786	57	7	above	above	ADP
ajst-20786	57	8	70	70	NUM
ajst-20786	57	9	%	%	NOUN
ajst-20786	57	10	,	,	PUNCT
ajst-20786	57	11	which	which	PRON
ajst-20786	57	12	proved	prove	VERB
ajst-20786	57	13	that	that	SCONJ
ajst-20786	57	14	it	it	PRON
ajst-20786	57	15	can	can	AUX
ajst-20786	57	16	handle	handle	VERB
ajst-20786	57	17	such	such	ADJ
ajst-20786	57	18	tasks	task	NOUN
ajst-20786	57	19	better	well	ADV
ajst-20786	57	20	.	.	PUNCT
ajst-20786	58	1	by	by	ADP
ajst-20786	58	2	comparison	comparison	NOUN
ajst-20786	58	3	,	,	PUNCT
ajst-20786	58	4	we	we	PRON
ajst-20786	58	5	can	can	AUX
ajst-20786	58	6	intuitively	intuitively	ADV
ajst-20786	58	7	observe	observe	VERB
ajst-20786	58	8	that	that	SCONJ
ajst-20786	58	9	there	there	PRON
ajst-20786	58	10	was	be	VERB
ajst-20786	58	11	little	little	ADJ
ajst-20786	58	12	difference	difference	NOUN
ajst-20786	58	13	in	in	ADP
ajst-20786	58	14	the	the	DET
ajst-20786	58	15	performance	performance	NOUN
ajst-20786	58	16	of	of	ADP
ajst-20786	58	17	amgc	amgc	NOUN
ajst-20786	58	18	tasks	task	NOUN
ajst-20786	58	19	between	between	ADP
ajst-20786	58	20	alexnet	alexnet	NOUN
ajst-20786	58	21	and	and	CCONJ
ajst-20786	58	22	googlenet	googlenet	NOUN
ajst-20786	58	23	in	in	ADP
ajst-20786	58	24	the	the	DET
ajst-20786	58	25	fma	fma	NOUN
ajst-20786	58	26	-	-	PUNCT
ajst-20786	58	27	small	small	ADJ
ajst-20786	58	28	dataset	dataset	NOUN
ajst-20786	58	29	.	.	PUNCT
ajst-20786	59	1	when	when	SCONJ
ajst-20786	59	2	it	it	PRON
ajst-20786	59	3	came	come	VERB
ajst-20786	59	4	to	to	ADP
ajst-20786	59	5	resnet-18	resnet-18	PROPN
ajst-20786	59	6	,	,	PUNCT
ajst-20786	59	7	the	the	DET
ajst-20786	59	8	average	average	ADJ
ajst-20786	59	9	accuracy	accuracy	NOUN
ajst-20786	59	10	significantly	significantly	ADV
ajst-20786	59	11	improved	improve	VERB
ajst-20786	59	12	to	to	ADP
ajst-20786	59	13	79.58	79.58	NUM
ajst-20786	59	14	%	%	NOUN
ajst-20786	59	15	,	,	PUNCT
ajst-20786	59	16	which	which	PRON
ajst-20786	59	17	is	be	AUX
ajst-20786	59	18	significantly	significantly	ADV
ajst-20786	59	19	different	different	ADJ
ajst-20786	59	20	from	from	ADP
ajst-20786	59	21	the	the	DET
ajst-20786	59	22	accuracy	accuracy	NOUN
ajst-20786	59	23	of	of	ADP
ajst-20786	59	24	the	the	DET
ajst-20786	59	25	previous	previous	ADJ
ajst-20786	59	26	two	two	NUM
ajst-20786	59	27	models	model	NOUN
ajst-20786	59	28	,	,	PUNCT
ajst-20786	59	29	which	which	PRON
ajst-20786	59	30	are	be	AUX
ajst-20786	59	31	50.89	50.89	NUM
ajst-20786	59	32	%	%	NOUN
ajst-20786	59	33	and	and	CCONJ
ajst-20786	59	34	51.53	51.53	NUM
ajst-20786	59	35	%	%	NOUN
ajst-20786	59	36	respectively	respectively	ADV
ajst-20786	59	37	.	.	PUNCT
ajst-20786	60	1	when	when	SCONJ
ajst-20786	60	2	we	we	PRON
ajst-20786	60	3	look	look	VERB
ajst-20786	60	4	at	at	ADP
ajst-20786	60	5	the	the	DET
ajst-20786	60	6	trends	trend	NOUN
ajst-20786	60	7	of	of	ADP
ajst-20786	60	8	the	the	DET
ajst-20786	60	9	accuracy	accuracy	NOUN
ajst-20786	60	10	of	of	ADP
ajst-20786	60	11	the	the	DET
ajst-20786	60	12	three	three	NUM
ajst-20786	60	13	neural	neural	ADJ
ajst-20786	60	14	networks	network	NOUN
ajst-20786	60	15	changing	change	VERB
ajst-20786	60	16	with	with	ADP
ajst-20786	60	17	the	the	DET
ajst-20786	60	18	number	number	NOUN
ajst-20786	60	19	of	of	ADP
ajst-20786	60	20	training	training	NOUN
ajst-20786	60	21	epochs	epoch	NOUN
ajst-20786	60	22	,	,	PUNCT
ajst-20786	60	23	it	it	PRON
ajst-20786	60	24	is	be	AUX
ajst-20786	60	25	obvious	obvious	ADJ
ajst-20786	60	26	that	that	SCONJ
ajst-20786	60	27	they	they	PRON
ajst-20786	60	28	all	all	PRON
ajst-20786	60	29	maintained	maintain	VERB
ajst-20786	60	30	a	a	DET
ajst-20786	60	31	similar	similar	ADJ
ajst-20786	60	32	upward	upward	ADJ
ajst-20786	60	33	trend	trend	NOUN
ajst-20786	60	34	,	,	PUNCT
ajst-20786	60	35	and	and	CCONJ
ajst-20786	60	36	the	the	DET
ajst-20786	60	37	accuracy	accuracy	NOUN
ajst-20786	60	38	differences	difference	NOUN
ajst-20786	60	39	were	be	AUX
ajst-20786	60	40	not	not	PART
ajst-20786	60	41	significant	significant	ADJ
ajst-20786	60	42	in	in	ADP
ajst-20786	60	43	the	the	DET
ajst-20786	60	44	first	first	ADJ
ajst-20786	60	45	24	24	NUM
ajst-20786	60	46	epochs	epoch	NOUN
ajst-20786	60	47	.	.	PUNCT
ajst-20786	61	1	from	from	ADP
ajst-20786	61	2	25th	25th	ADJ
ajst-20786	61	3	epoch	epoch	NOUN
ajst-20786	61	4	to	to	ADP
ajst-20786	61	5	50th	50th	ADJ
ajst-20786	61	6	epoch	epoch	NOUN
ajst-20786	61	7	,	,	PUNCT
ajst-20786	61	8	only	only	ADV
ajst-20786	61	9	resnet-18	resnet-18	PROPN
ajst-20786	61	10	still	still	ADV
ajst-20786	61	11	kept	keep	VERB
ajst-20786	61	12	its	its	PRON
ajst-20786	61	13	upward	upward	ADJ
ajst-20786	61	14	trend	trend	NOUN
ajst-20786	61	15	.	.	PUNCT
ajst-20786	62	1	by	by	ADP
ajst-20786	62	2	referring	refer	VERB
ajst-20786	62	3	to	to	PART
ajst-20786	62	4	figure	figure	VERB
ajst-20786	62	5	3	3	NUM
ajst-20786	62	6	,	,	PUNCT
ajst-20786	62	7	you	you	PRON
ajst-20786	62	8	can	can	AUX
ajst-20786	62	9	more	more	ADV
ajst-20786	62	10	intuitively	intuitively	ADV
ajst-20786	62	11	clarify	clarify	VERB
ajst-20786	62	12	the	the	DET
ajst-20786	62	13	above	above	ADJ
ajst-20786	62	14	conclusion	conclusion	NOUN
ajst-20786	62	15	.	.	PUNCT
ajst-20786	63	1	figure	figure	NOUN
ajst-20786	63	2	3	3	NUM
ajst-20786	63	3	.	.	NOUN
ajst-20786	63	4	comparison	comparison	NOUN
ajst-20786	63	5	among	among	ADP
ajst-20786	63	6	three	three	NUM
ajst-20786	63	7	models	model	NOUN
ajst-20786	63	8	4	4	NUM
ajst-20786	63	9	.	.	PUNCT
ajst-20786	63	10	analysis	analysis	NOUN
ajst-20786	63	11	4.1	4.1	NUM
ajst-20786	63	12	.	.	PUNCT
ajst-20786	64	1	the	the	DET
ajst-20786	64	2	characteristics	characteristic	NOUN
ajst-20786	64	3	of	of	ADP
ajst-20786	64	4	the	the	DET
ajst-20786	64	5	experiment	experiment	NOUN
ajst-20786	64	6	in	in	ADP
ajst-20786	64	7	comparison	comparison	NOUN
ajst-20786	64	8	with	with	ADP
ajst-20786	64	9	datasets	dataset	NOUN
ajst-20786	64	10	such	such	ADJ
ajst-20786	64	11	as	as	ADP
ajst-20786	64	12	million	million	NUM
ajst-20786	64	13	song	song	NOUN
ajst-20786	64	14	dataset	dataset	NOUN
ajst-20786	64	15	(	(	PUNCT
ajst-20786	64	16	msd	msd	NOUN
ajst-20786	64	17	)	)	PUNCT
ajst-20786	64	18	,	,	PUNCT
ajst-20786	64	19	million	million	NUM
ajst-20786	64	20	musical	musical	ADJ
ajst-20786	64	21	tweets	tweet	NOUN
ajst-20786	64	22	dataset	dataset	NOUN
ajst-20786	64	23	(	(	PUNCT
ajst-20786	64	24	mmtd	mmtd	NOUN
ajst-20786	64	25	)	)	PUNCT
ajst-20786	64	26	,	,	PUNCT
ajst-20786	64	27	fmasmall	fmasmall	PROPN
ajst-20786	64	28	contains	contain	VERB
ajst-20786	64	29	fewer	few	ADJ
ajst-20786	64	30	number	number	NOUN
ajst-20786	64	31	of	of	ADP
ajst-20786	64	32	music	music	NOUN
ajst-20786	64	33	clips	clip	NOUN
ajst-20786	64	34	,	,	PUNCT
ajst-20786	64	35	which	which	PRON
ajst-20786	64	36	made	make	VERB
ajst-20786	64	37	the	the	DET
ajst-20786	64	38	experiment	experiment	NOUN
ajst-20786	64	39	kind	kind	NOUN
ajst-20786	64	40	of	of	ADV
ajst-20786	64	41	tricky	tricky	ADJ
ajst-20786	64	42	.	.	PUNCT
ajst-20786	65	1	to	to	PART
ajst-20786	65	2	be	be	AUX
ajst-20786	65	3	more	more	ADV
ajst-20786	65	4	precise	precise	ADJ
ajst-20786	65	5	,	,	PUNCT
ajst-20786	65	6	the	the	DET
ajst-20786	65	7	models	model	NOUN
ajst-20786	65	8	need	need	VERB
ajst-20786	65	9	to	to	PART
ajst-20786	65	10	classify	classify	VERB
ajst-20786	65	11	the	the	DET
ajst-20786	65	12	genres	genre	NOUN
ajst-20786	65	13	while	while	SCONJ
ajst-20786	65	14	adjusting	adjust	VERB
ajst-20786	65	15	the	the	DET
ajst-20786	65	16	limited	limited	ADJ
ajst-20786	65	17	amount	amount	NOUN
ajst-20786	65	18	of	of	ADP
ajst-20786	65	19	training	training	NOUN
ajst-20786	65	20	samples	sample	NOUN
ajst-20786	65	21	in	in	ADP
ajst-20786	65	22	order	order	NOUN
ajst-20786	65	23	to	to	PART
ajst-20786	65	24	show	show	VERB
ajst-20786	65	25	a	a	DET
ajst-20786	65	26	satisfying	satisfy	VERB
ajst-20786	65	27	performance	performance	NOUN
ajst-20786	65	28	.	.	PUNCT
ajst-20786	66	1	meanwhile	meanwhile	ADV
ajst-20786	66	2	,	,	PUNCT
ajst-20786	66	3	we	we	PRON
ajst-20786	66	4	also	also	ADV
ajst-20786	66	5	used	use	VERB
ajst-20786	66	6	the	the	DET
ajst-20786	66	7	running	running	ADJ
ajst-20786	66	8	time	time	NOUN
ajst-20786	66	9	of	of	ADP
ajst-20786	66	10	each	each	DET
ajst-20786	66	11	dataset	dataset	NOUN
ajst-20786	66	12	as	as	ADP
ajst-20786	66	13	a	a	DET
ajst-20786	66	14	reference	reference	NOUN
ajst-20786	66	15	when	when	SCONJ
ajst-20786	66	16	judging	judge	VERB
ajst-20786	66	17	the	the	DET
ajst-20786	66	18	comprehensive	comprehensive	ADJ
ajst-20786	66	19	performance	performance	NOUN
ajst-20786	66	20	of	of	ADP
ajst-20786	66	21	those	those	DET
ajst-20786	66	22	models	model	NOUN
ajst-20786	66	23	in	in	ADP
ajst-20786	66	24	this	this	DET
ajst-20786	66	25	experiment	experiment	NOUN
ajst-20786	66	26	.	.	PUNCT
ajst-20786	67	1	4.2	4.2	NUM
ajst-20786	67	2	.	.	PUNCT
ajst-20786	68	1	the	the	DET
ajst-20786	68	2	similar	similar	ADJ
ajst-20786	68	3	performance	performance	NOUN
ajst-20786	68	4	between	between	ADP
ajst-20786	68	5	alexnet	alexnet	NOUN
ajst-20786	68	6	and	and	CCONJ
ajst-20786	68	7	googlenet	googlenet	NOUN
ajst-20786	68	8	as	as	SCONJ
ajst-20786	68	9	we	we	PRON
ajst-20786	68	10	refer	refer	VERB
ajst-20786	68	11	to	to	PART
ajst-20786	68	12	figure	figure	VERB
ajst-20786	68	13	3	3	NUM
ajst-20786	68	14	above	above	ADV
ajst-20786	68	15	,	,	PUNCT
ajst-20786	68	16	we	we	PRON
ajst-20786	68	17	can	can	AUX
ajst-20786	68	18	easily	easily	ADV
ajst-20786	68	19	find	find	VERB
ajst-20786	68	20	that	that	SCONJ
ajst-20786	68	21	the	the	DET
ajst-20786	68	22	accuracy	accuracy	NOUN
ajst-20786	68	23	of	of	ADP
ajst-20786	68	24	alexnet	alexnet	NOUN
ajst-20786	68	25	was	be	AUX
ajst-20786	68	26	similar	similar	ADJ
ajst-20786	68	27	to	to	ADP
ajst-20786	68	28	the	the	DET
ajst-20786	68	29	accuracy	accuracy	NOUN
ajst-20786	68	30	of	of	ADP
ajst-20786	68	31	googlenet	googlenet	NOUN
ajst-20786	68	32	during	during	ADP
ajst-20786	68	33	every	every	DET
ajst-20786	68	34	epoch	epoch	NOUN
ajst-20786	68	35	.	.	PUNCT
ajst-20786	69	1	in	in	ADP
ajst-20786	69	2	specific	specific	ADJ
ajst-20786	69	3	epochs	epoch	NOUN
ajst-20786	69	4	like	like	ADP
ajst-20786	69	5	epoch	epoch	NOUN
ajst-20786	69	6	9	9	NUM
ajst-20786	69	7	,	,	PUNCT
ajst-20786	69	8	googlenet	googlenet	NOUN
ajst-20786	69	9	performed	perform	VERB
ajst-20786	69	10	even	even	ADV
ajst-20786	69	11	worse	bad	ADJ
ajst-20786	69	12	than	than	ADP
ajst-20786	69	13	alexnet	alexnet	ADJ
ajst-20786	69	14	.	.	PUNCT
ajst-20786	70	1	also	also	ADV
ajst-20786	70	2	,	,	PUNCT
ajst-20786	70	3	the	the	DET
ajst-20786	70	4	running	running	ADJ
ajst-20786	70	5	time	time	NOUN
ajst-20786	70	6	of	of	ADP
ajst-20786	70	7	googlenet	googlenet	NOUN
ajst-20786	70	8	was	be	AUX
ajst-20786	70	9	relatively	relatively	ADV
ajst-20786	70	10	longer	long	ADJ
ajst-20786	70	11	.	.	PUNCT
ajst-20786	71	1	to	to	PART
ajst-20786	71	2	comprehend	comprehend	VERB
ajst-20786	71	3	such	such	ADJ
ajst-20786	71	4	result	result	NOUN
ajst-20786	71	5	above	above	ADV
ajst-20786	71	6	,	,	PUNCT
ajst-20786	71	7	we	we	PRON
ajst-20786	71	8	could	could	AUX
ajst-20786	71	9	start	start	VERB
ajst-20786	71	10	from	from	ADP
ajst-20786	71	11	the	the	DET
ajst-20786	71	12	structures	structure	NOUN
ajst-20786	71	13	of	of	ADP
ajst-20786	71	14	the	the	DET
ajst-20786	71	15	two	two	NUM
ajst-20786	71	16	models	model	NOUN
ajst-20786	71	17	.	.	PUNCT
ajst-20786	72	1	both	both	CCONJ
ajst-20786	72	2	the	the	DET
ajst-20786	72	3	input	input	NOUN
ajst-20786	72	4	of	of	ADP
ajst-20786	72	5	alexnet	alexnet	NOUN
ajst-20786	72	6	and	and	CCONJ
ajst-20786	72	7	the	the	DET
ajst-20786	72	8	input	input	NOUN
ajst-20786	72	9	of	of	ADP
ajst-20786	72	10	googlenet	googlenet	NOUN
ajst-20786	72	11	should	should	AUX
ajst-20786	72	12	be	be	AUX
ajst-20786	72	13	images	image	NOUN
ajst-20786	72	14	of	of	ADP
ajst-20786	72	15	pixel	pixel	ADJ
ajst-20786	72	16	size	size	NOUN
ajst-20786	72	17	and	and	CCONJ
ajst-20786	72	18	of	of	ADP
ajst-20786	72	19	three	three	NUM
ajst-20786	72	20	channels	channel	NOUN
ajst-20786	72	21	r	r	NOUN
ajst-20786	72	22	,	,	PUNCT
ajst-20786	72	23	g	g	PROPN
ajst-20786	72	24	and	and	CCONJ
ajst-20786	72	25	b.	b.	PROPN
ajst-20786	72	26	both	both	CCONJ
ajst-20786	72	27	the	the	DET
ajst-20786	72	28	two	two	NUM
ajst-20786	72	29	models	model	NOUN
ajst-20786	72	30	applied	apply	VERB
ajst-20786	72	31	the	the	DET
ajst-20786	72	32	pretreatment	pretreatment	NOUN
ajst-20786	72	33	technology	technology	NOUN
ajst-20786	72	34	like	like	ADP
ajst-20786	72	35	data	datum	NOUN
ajst-20786	72	36	augmentation	augmentation	NOUN
ajst-20786	72	37	and	and	CCONJ
ajst-20786	72	38	zero	zero	NUM
ajst-20786	72	39	mean	mean	NOUN
ajst-20786	72	40	normalization	normalization	NOUN
ajst-20786	72	41	.	.	PUNCT
ajst-20786	73	1	such	such	ADJ
ajst-20786	73	2	technology	technology	NOUN
ajst-20786	73	3	could	could	AUX
ajst-20786	73	4	strengthen	strengthen	VERB
ajst-20786	73	5	the	the	DET
ajst-20786	73	6	generalization	generalization	NOUN
ajst-20786	73	7	of	of	ADP
ajst-20786	73	8	models	model	NOUN
ajst-20786	73	9	,	,	PUNCT
ajst-20786	73	10	while	while	SCONJ
ajst-20786	73	11	it	it	PRON
ajst-20786	73	12	may	may	AUX
ajst-20786	73	13	also	also	ADV
ajst-20786	73	14	carry	carry	VERB
ajst-20786	73	15	the	the	DET
ajst-20786	73	16	risk	risk	NOUN
ajst-20786	73	17	of	of	ADP
ajst-20786	73	18	making	make	VERB
ajst-20786	73	19	the	the	DET
ajst-20786	73	20	them	they	PRON
ajst-20786	73	21	overly	overly	ADV
ajst-20786	73	22	sensitive	sensitive	ADJ
ajst-20786	73	23	to	to	ADP
ajst-20786	73	24	certain	certain	ADJ
ajst-20786	73	25	features	feature	NOUN
ajst-20786	73	26	of	of	ADP
ajst-20786	73	27	the	the	DET
ajst-20786	73	28	images	image	NOUN
ajst-20786	73	29	(	(	PUNCT
ajst-20786	73	30	even	even	ADV
ajst-20786	73	31	though	though	SCONJ
ajst-20786	73	32	some	some	PRON
ajst-20786	73	33	of	of	ADP
ajst-20786	73	34	them	they	PRON
ajst-20786	73	35	are	be	AUX
ajst-20786	73	36	inconsequential	inconsequential	ADJ
ajst-20786	73	37	)	)	PUNCT
ajst-20786	73	38	.	.	PUNCT
ajst-20786	74	1	in	in	ADP
ajst-20786	74	2	addition	addition	NOUN
ajst-20786	74	3	,	,	PUNCT
ajst-20786	74	4	the	the	DET
ajst-20786	74	5	use	use	NOUN
ajst-20786	74	6	of	of	ADP
ajst-20786	74	7	local	local	ADJ
ajst-20786	74	8	response	response	NOUN
ajst-20786	74	9	normalization	normalization	NOUN
ajst-20786	74	10	(	(	PUNCT
ajst-20786	74	11	lrn	lrn	PROPN
ajst-20786	74	12	)	)	PUNCT
ajst-20786	74	13	could	could	AUX
ajst-20786	74	14	also	also	ADV
ajst-20786	74	15	take	take	VERB
ajst-20786	74	16	responsibility	responsibility	NOUN
ajst-20786	74	17	for	for	ADP
ajst-20786	74	18	the	the	DET
ajst-20786	74	19	deterioration	deterioration	NOUN
ajst-20786	74	20	of	of	ADP
ajst-20786	74	21	models	model	NOUN
ajst-20786	74	22	.	.	PUNCT
ajst-20786	75	1	[	[	X
ajst-20786	75	2	9	9	NUM
ajst-20786	75	3	-	-	SYM
ajst-20786	75	4	10	10	NUM
ajst-20786	75	5	]	]	SYM
ajst-20786	75	6	77	77	NUM
ajst-20786	75	7	4.3	4.3	NUM
ajst-20786	75	8	.	.	PUNCT
ajst-20786	76	1	better	well	ADJ
ajst-20786	76	2	performance	performance	NOUN
ajst-20786	76	3	from	from	ADP
ajst-20786	76	4	resnet-18	resnet-18	PROPN
ajst-20786	76	5	the	the	DET
ajst-20786	76	6	accuracy	accuracy	NOUN
ajst-20786	76	7	of	of	ADP
ajst-20786	76	8	resnet-18	resnet-18	PROPN
ajst-20786	76	9	kept	keep	VERB
ajst-20786	76	10	an	an	DET
ajst-20786	76	11	increasing	increase	VERB
ajst-20786	76	12	trend	trend	NOUN
ajst-20786	76	13	after	after	ADP
ajst-20786	76	14	epoch	epoch	NOUN
ajst-20786	76	15	25	25	NUM
ajst-20786	76	16	.	.	PUNCT
ajst-20786	77	1	as	as	SCONJ
ajst-20786	77	2	it	it	PRON
ajst-20786	77	3	reached	reach	VERB
ajst-20786	77	4	70	70	NUM
ajst-20786	77	5	%	%	NOUN
ajst-20786	77	6	,	,	PUNCT
ajst-20786	77	7	the	the	DET
ajst-20786	77	8	rising	rise	VERB
ajst-20786	77	9	rate	rate	NOUN
ajst-20786	77	10	shrank	shrink	VERB
ajst-20786	77	11	gradually	gradually	ADV
ajst-20786	77	12	.	.	PUNCT
ajst-20786	78	1	thus	thus	ADV
ajst-20786	78	2	,	,	PUNCT
ajst-20786	78	3	we	we	PRON
ajst-20786	78	4	can	can	AUX
ajst-20786	78	5	consider	consider	VERB
ajst-20786	78	6	that	that	SCONJ
ajst-20786	78	7	the	the	DET
ajst-20786	78	8	structure	structure	NOUN
ajst-20786	78	9	of	of	ADP
ajst-20786	78	10	resnet-18	resnet-18	PROPN
ajst-20786	78	11	performed	perform	VERB
ajst-20786	78	12	better	well	ADV
ajst-20786	78	13	in	in	ADP
ajst-20786	78	14	such	such	ADJ
ajst-20786	78	15	kind	kind	NOUN
ajst-20786	78	16	of	of	ADP
ajst-20786	78	17	amgc	amgc	NOUN
ajst-20786	78	18	tasks	task	NOUN
ajst-20786	78	19	.	.	PUNCT
ajst-20786	79	1	ye	ye	PRON
ajst-20786	80	1	j	j	PROPN
ajst-20786	80	2	c	c	VERB
ajst-20786	80	3	et	et	PROPN
ajst-20786	80	4	al	al	PROPN
ajst-20786	80	5	.	.	PROPN
ajst-20786	80	6	,	,	PUNCT
ajst-20786	80	7	the	the	DET
ajst-20786	80	8	authors	author	NOUN
ajst-20786	80	9	of	of	ADP
ajst-20786	80	10	resnet	resnet	NOUN
ajst-20786	80	11	,	,	PUNCT
ajst-20786	80	12	had	have	AUX
ajst-20786	80	13	tested	test	VERB
ajst-20786	80	14	a	a	DET
ajst-20786	80	15	model	model	NOUN
ajst-20786	80	16	by	by	ADP
ajst-20786	80	17	dataset	dataset	NOUN
ajst-20786	80	18	cifar10	cifar10	PROPN
ajst-20786	80	19	.	.	PUNCT
ajst-20786	81	1	they	they	PRON
ajst-20786	81	2	got	get	VERB
ajst-20786	81	3	a	a	DET
ajst-20786	81	4	conclusion	conclusion	NOUN
ajst-20786	81	5	through	through	ADP
ajst-20786	81	6	the	the	DET
ajst-20786	81	7	strange	strange	ADJ
ajst-20786	81	8	result	result	NOUN
ajst-20786	81	9	that	that	SCONJ
ajst-20786	81	10	the	the	DET
ajst-20786	81	11	model	model	NOUN
ajst-20786	81	12	with	with	ADP
ajst-20786	81	13	56	56	NUM
ajst-20786	81	14	layers	layer	NOUN
ajst-20786	81	15	made	make	VERB
ajst-20786	81	16	more	more	ADJ
ajst-20786	81	17	mistakes	mistake	NOUN
ajst-20786	81	18	than	than	ADP
ajst-20786	81	19	that	that	DET
ajst-20786	81	20	one	one	NUM
ajst-20786	81	21	with	with	ADP
ajst-20786	81	22	only	only	ADV
ajst-20786	81	23	20	20	NUM
ajst-20786	81	24	layers	layer	NOUN
ajst-20786	81	25	:	:	PUNCT
ajst-20786	81	26	adding	add	VERB
ajst-20786	81	27	too	too	ADV
ajst-20786	81	28	much	much	ADJ
ajst-20786	81	29	layers	layer	NOUN
ajst-20786	81	30	in	in	ADP
ajst-20786	81	31	a	a	DET
ajst-20786	81	32	neural	neural	ADJ
ajst-20786	81	33	network	network	NOUN
ajst-20786	81	34	could	could	AUX
ajst-20786	81	35	increase	increase	VERB
ajst-20786	81	36	the	the	DET
ajst-20786	81	37	risk	risk	NOUN
ajst-20786	81	38	of	of	ADP
ajst-20786	81	39	neural	neural	ADJ
ajst-20786	81	40	network	network	NOUN
ajst-20786	81	41	degradation	degradation	NOUN
ajst-20786	81	42	(	(	PUNCT
ajst-20786	81	43	the	the	DET
ajst-20786	81	44	back	back	NOUN
ajst-20786	81	45	features	feature	NOUN
ajst-20786	81	46	have	have	AUX
ajst-20786	81	47	almost	almost	ADV
ajst-20786	81	48	lost	lose	VERB
ajst-20786	81	49	the	the	DET
ajst-20786	81	50	original	original	ADJ
ajst-20786	81	51	appearance	appearance	NOUN
ajst-20786	81	52	of	of	ADP
ajst-20786	81	53	the	the	DET
ajst-20786	81	54	front	front	ADJ
ajst-20786	81	55	features	feature	NOUN
ajst-20786	81	56	)	)	PUNCT
ajst-20786	81	57	.	.	PUNCT
ajst-20786	82	1	through	through	ADP
ajst-20786	82	2	the	the	DET
ajst-20786	82	3	application	application	NOUN
ajst-20786	82	4	of	of	ADP
ajst-20786	82	5	inter	inter	ADJ
ajst-20786	82	6	layer	layer	NOUN
ajst-20786	82	7	residual	residual	ADJ
ajst-20786	82	8	hopping	hopping	NOUN
ajst-20786	82	9	,	,	PUNCT
ajst-20786	82	10	resnet	resnet	NOUN
ajst-20786	82	11	introduces	introduce	NOUN
ajst-20786	82	12	forward	forward	ADV
ajst-20786	82	13	information	information	NOUN
ajst-20786	82	14	,	,	PUNCT
ajst-20786	82	15	alleviates	alleviate	VERB
ajst-20786	82	16	the	the	DET
ajst-20786	82	17	phenomenon	phenomenon	NOUN
ajst-20786	82	18	of	of	ADP
ajst-20786	82	19	gradient	gradient	ADJ
ajst-20786	82	20	vanishing	vanishing	NOUN
ajst-20786	82	21	,	,	PUNCT
ajst-20786	82	22	and	and	CCONJ
ajst-20786	82	23	makes	make	VERB
ajst-20786	82	24	it	it	PRON
ajst-20786	82	25	possible	possible	ADJ
ajst-20786	82	26	to	to	PART
ajst-20786	82	27	increase	increase	VERB
ajst-20786	82	28	the	the	DET
ajst-20786	82	29	number	number	NOUN
ajst-20786	82	30	of	of	ADP
ajst-20786	82	31	layers	layer	NOUN
ajst-20786	82	32	in	in	ADP
ajst-20786	82	33	the	the	DET
ajst-20786	82	34	neural	neural	ADJ
ajst-20786	82	35	network	network	NOUN
ajst-20786	82	36	with	with	ADP
ajst-20786	82	37	a	a	DET
ajst-20786	82	38	better	well	ADJ
ajst-20786	82	39	accuracy	accuracy	NOUN
ajst-20786	82	40	.	.	PUNCT
ajst-20786	83	1	4.4	4.4	NUM
ajst-20786	83	2	.	.	PUNCT
ajst-20786	84	1	the	the	DET
ajst-20786	84	2	accuracy	accuracy	NOUN
ajst-20786	84	3	of	of	ADP
ajst-20786	84	4	classifying	classify	VERB
ajst-20786	84	5	specific	specific	ADJ
ajst-20786	84	6	genres	genre	NOUN
ajst-20786	84	7	through	through	ADP
ajst-20786	84	8	the	the	DET
ajst-20786	84	9	experiment	experiment	NOUN
ajst-20786	84	10	,	,	PUNCT
ajst-20786	84	11	the	the	DET
ajst-20786	84	12	result	result	NOUN
ajst-20786	84	13	told	tell	VERB
ajst-20786	84	14	us	we	PRON
ajst-20786	84	15	that	that	SCONJ
ajst-20786	84	16	alexnet	alexnet	NOUN
ajst-20786	84	17	had	have	VERB
ajst-20786	84	18	the	the	DET
ajst-20786	84	19	highest	high	ADJ
ajst-20786	84	20	accuracy	accuracy	NOUN
ajst-20786	84	21	(	(	PUNCT
ajst-20786	84	22	roughly	roughly	ADV
ajst-20786	84	23	75	75	NUM
ajst-20786	84	24	%	%	NOUN
ajst-20786	84	25	)	)	PUNCT
ajst-20786	84	26	classifying	classify	VERB
ajst-20786	84	27	the	the	DET
ajst-20786	84	28	hip	hip	NOUN
ajst-20786	84	29	-	-	PUNCT
ajst-20786	84	30	hop	hop	NOUN
ajst-20786	84	31	music	music	NOUN
ajst-20786	84	32	than	than	ADP
ajst-20786	84	33	other	other	ADJ
ajst-20786	84	34	catagories	catagorie	NOUN
ajst-20786	84	35	.	.	PUNCT
ajst-20786	85	1	the	the	DET
ajst-20786	85	2	difference	difference	NOUN
ajst-20786	85	3	in	in	ADP
ajst-20786	85	4	recognition	recognition	NOUN
ajst-20786	85	5	accuracy	accuracy	NOUN
ajst-20786	85	6	of	of	ADP
ajst-20786	85	7	googlenet	googlenet	NOUN
ajst-20786	85	8	for	for	ADP
ajst-20786	85	9	these	these	DET
ajst-20786	85	10	specific	specific	ADJ
ajst-20786	85	11	genres	genre	NOUN
ajst-20786	85	12	is	be	AUX
ajst-20786	85	13	relatively	relatively	ADV
ajst-20786	85	14	small	small	ADJ
ajst-20786	85	15	,	,	PUNCT
ajst-20786	85	16	but	but	CCONJ
ajst-20786	85	17	in	in	ADP
ajst-20786	85	18	comparison	comparison	NOUN
ajst-20786	85	19	,	,	PUNCT
ajst-20786	85	20	it	it	PRON
ajst-20786	85	21	has	have	VERB
ajst-20786	85	22	a	a	DET
ajst-20786	85	23	stronger	strong	ADJ
ajst-20786	85	24	ability	ability	NOUN
ajst-20786	85	25	to	to	PART
ajst-20786	85	26	judge	judge	VERB
ajst-20786	85	27	pop	pop	NOUN
ajst-20786	85	28	music	music	NOUN
ajst-20786	85	29	and	and	CCONJ
ajst-20786	85	30	folk	folk	NOUN
ajst-20786	85	31	music	music	NOUN
ajst-20786	85	32	.	.	PUNCT
ajst-20786	86	1	overall	overall	ADJ
ajst-20786	86	2	,	,	PUNCT
ajst-20786	86	3	resnet-18	resnet-18	PROPN
ajst-20786	86	4	was	be	AUX
ajst-20786	86	5	better	well	ADV
ajst-20786	86	6	able	able	ADJ
ajst-20786	86	7	to	to	PART
ajst-20786	86	8	recognize	recognize	VERB
ajst-20786	86	9	any	any	DET
ajst-20786	86	10	type	type	NOUN
ajst-20786	86	11	of	of	ADP
ajst-20786	86	12	music	music	NOUN
ajst-20786	86	13	among	among	ADP
ajst-20786	86	14	those	those	DET
ajst-20786	86	15	models	model	NOUN
ajst-20786	86	16	.	.	PUNCT
ajst-20786	87	1	and	and	CCONJ
ajst-20786	87	2	it	it	PRON
ajst-20786	87	3	was	be	AUX
ajst-20786	87	4	better	well	ADJ
ajst-20786	87	5	at	at	ADP
ajst-20786	87	6	finding	find	VERB
ajst-20786	87	7	international	international	ADJ
ajst-20786	87	8	,	,	PUNCT
ajst-20786	87	9	electronic	electronic	ADJ
ajst-20786	87	10	,	,	PUNCT
ajst-20786	87	11	pop	pop	NOUN
ajst-20786	87	12	and	and	CCONJ
ajst-20786	87	13	jazz	jazz	NOUN
ajst-20786	87	14	music	music	NOUN
ajst-20786	87	15	(	(	PUNCT
ajst-20786	87	16	with	with	ADP
ajst-20786	87	17	an	an	DET
ajst-20786	87	18	accuracy	accuracy	NOUN
ajst-20786	87	19	rate	rate	NOUN
ajst-20786	87	20	of	of	ADP
ajst-20786	87	21	over	over	ADP
ajst-20786	87	22	60	60	NUM
ajst-20786	87	23	%	%	NOUN
ajst-20786	87	24	)	)	PUNCT
ajst-20786	87	25	.	.	PUNCT
ajst-20786	88	1	if	if	SCONJ
ajst-20786	88	2	we	we	PRON
ajst-20786	88	3	divide	divide	VERB
ajst-20786	88	4	music	music	NOUN
ajst-20786	88	5	into	into	ADP
ajst-20786	88	6	two	two	NUM
ajst-20786	88	7	categories	category	NOUN
ajst-20786	88	8	based	base	VERB
ajst-20786	88	9	on	on	ADP
ajst-20786	88	10	accuracy	accuracy	NOUN
ajst-20786	88	11	,	,	PUNCT
ajst-20786	88	12	"	"	PUNCT
ajst-20786	88	13	strong	strong	ADJ
ajst-20786	88	14	correlation	correlation	NOUN
ajst-20786	88	15	between	between	ADP
ajst-20786	88	16	accuracy	accuracy	NOUN
ajst-20786	88	17	and	and	CCONJ
ajst-20786	88	18	the	the	DET
ajst-20786	88	19	model	model	NOUN
ajst-20786	88	20	used	use	VERB
ajst-20786	88	21	"	"	PUNCT
ajst-20786	88	22	and	and	CCONJ
ajst-20786	88	23	"	"	PUNCT
ajst-20786	88	24	weak	weak	ADJ
ajst-20786	88	25	correlation	correlation	NOUN
ajst-20786	88	26	between	between	ADP
ajst-20786	88	27	accuracy	accuracy	NOUN
ajst-20786	88	28	and	and	CCONJ
ajst-20786	88	29	the	the	DET
ajst-20786	88	30	model	model	NOUN
ajst-20786	88	31	used	use	VERB
ajst-20786	88	32	"	"	PUNCT
ajst-20786	88	33	,	,	PUNCT
ajst-20786	88	34	then	then	ADV
ajst-20786	88	35	the	the	DET
ajst-20786	88	36	former	former	ADJ
ajst-20786	88	37	would	would	AUX
ajst-20786	88	38	be	be	AUX
ajst-20786	88	39	represented	represent	VERB
ajst-20786	88	40	by	by	ADP
ajst-20786	88	41	international	international	ADJ
ajst-20786	88	42	music	music	NOUN
ajst-20786	88	43	,	,	PUNCT
ajst-20786	88	44	while	while	SCONJ
ajst-20786	88	45	the	the	DET
ajst-20786	88	46	latter	latter	ADJ
ajst-20786	88	47	are	be	AUX
ajst-20786	88	48	most	most	ADV
ajst-20786	88	49	representative	representative	ADJ
ajst-20786	88	50	by	by	ADP
ajst-20786	88	51	instrumental	instrumental	ADJ
ajst-20786	88	52	and	and	CCONJ
ajst-20786	88	53	blues	blue	NOUN
ajst-20786	88	54	music	music	NOUN
ajst-20786	88	55	.	.	PUNCT
ajst-20786	89	1	figure	figure	NOUN
ajst-20786	89	2	4	4	NUM
ajst-20786	89	3	.	.	PUNCT
ajst-20786	89	4	comparison	comparison	NOUN
ajst-20786	89	5	about	about	ADP
ajst-20786	89	6	accuracy	accuracy	NOUN
ajst-20786	89	7	among	among	ADP
ajst-20786	89	8	different	different	ADJ
ajst-20786	89	9	models	model	NOUN
ajst-20786	89	10	and	and	CCONJ
ajst-20786	89	11	music	music	NOUN
ajst-20786	89	12	genres	genre	NOUN
ajst-20786	89	13	5	5	NUM
ajst-20786	89	14	.	.	PUNCT
ajst-20786	90	1	conclusions	conclusion	NOUN
ajst-20786	90	2	we	we	PRON
ajst-20786	90	3	are	be	AUX
ajst-20786	90	4	in	in	ADP
ajst-20786	90	5	the	the	DET
ajst-20786	90	6	developing	develop	VERB
ajst-20786	90	7	period	period	NOUN
ajst-20786	90	8	of	of	ADP
ajst-20786	90	9	digital	digital	ADJ
ajst-20786	90	10	audio	audio	ADJ
ajst-20786	90	11	market	market	NOUN
ajst-20786	90	12	.	.	PUNCT
ajst-20786	91	1	it	it	PRON
ajst-20786	91	2	will	will	AUX
ajst-20786	91	3	undoubtfully	undoubtfully	ADV
ajst-20786	91	4	incur	incur	VERB
ajst-20786	91	5	astonishing	astonishing	ADJ
ajst-20786	91	6	labor	labor	NOUN
ajst-20786	91	7	and	and	CCONJ
ajst-20786	91	8	time	time	NOUN
ajst-20786	91	9	cost	cost	NOUN
ajst-20786	91	10	if	if	SCONJ
ajst-20786	91	11	we	we	PRON
ajst-20786	91	12	manually	manually	ADV
ajst-20786	91	13	add	add	VERB
ajst-20786	91	14	genre	genre	NOUN
ajst-20786	91	15	labels	label	NOUN
ajst-20786	91	16	to	to	ADP
ajst-20786	91	17	those	those	DET
ajst-20786	91	18	increasingly	increasingly	ADV
ajst-20786	91	19	new	new	ADJ
ajst-20786	91	20	music	music	NOUN
ajst-20786	91	21	and	and	CCONJ
ajst-20786	91	22	old	old	ADJ
ajst-20786	91	23	music	music	NOUN
ajst-20786	91	24	lacking	lack	VERB
ajst-20786	91	25	metadata	metadata	NOUN
ajst-20786	91	26	.	.	PUNCT
ajst-20786	92	1	and	and	CCONJ
ajst-20786	92	2	accurate	accurate	ADJ
ajst-20786	92	3	genre	genre	NOUN
ajst-20786	92	4	information	information	NOUN
ajst-20786	92	5	is	be	AUX
ajst-20786	92	6	crucial	crucial	ADJ
ajst-20786	92	7	for	for	SCONJ
ajst-20786	92	8	various	various	ADJ
ajst-20786	92	9	music	music	NOUN
ajst-20786	92	10	playback	playback	NOUN
ajst-20786	92	11	platforms	platform	NOUN
ajst-20786	92	12	to	to	PART
ajst-20786	92	13	effectively	effectively	ADV
ajst-20786	92	14	push	push	VERB
ajst-20786	92	15	big	big	ADJ
ajst-20786	92	16	data	datum	NOUN
ajst-20786	92	17	to	to	ADP
ajst-20786	92	18	users	user	NOUN
ajst-20786	92	19	today	today	NOUN
ajst-20786	92	20	.	.	PUNCT
ajst-20786	93	1	in	in	ADP
ajst-20786	93	2	the	the	DET
ajst-20786	93	3	era	era	NOUN
ajst-20786	93	4	of	of	ADP
ajst-20786	93	5	the	the	DET
ajst-20786	93	6	rise	rise	NOUN
ajst-20786	93	7	of	of	ADP
ajst-20786	93	8	convolutional	convolutional	ADJ
ajst-20786	93	9	neural	neural	ADJ
ajst-20786	93	10	networks	network	NOUN
ajst-20786	93	11	,	,	PUNCT
ajst-20786	93	12	more	more	ADJ
ajst-20786	93	13	and	and	CCONJ
ajst-20786	93	14	more	more	ADJ
ajst-20786	93	15	industry	industry	NOUN
ajst-20786	93	16	professionals	professional	NOUN
ajst-20786	93	17	are	be	AUX
ajst-20786	93	18	attempting	attempt	VERB
ajst-20786	93	19	to	to	PART
ajst-20786	93	20	use	use	VERB
ajst-20786	93	21	these	these	DET
ajst-20786	93	22	models	model	NOUN
ajst-20786	93	23	for	for	ADP
ajst-20786	93	24	amgc	amgc	NOUN
ajst-20786	93	25	tasks	task	NOUN
ajst-20786	93	26	.	.	PUNCT
ajst-20786	94	1	the	the	DET
ajst-20786	94	2	article	article	NOUN
ajst-20786	94	3	referred	refer	VERB
ajst-20786	94	4	to	to	ADP
ajst-20786	94	5	the	the	DET
ajst-20786	94	6	relevant	relevant	ADJ
ajst-20786	94	7	theories	theory	NOUN
ajst-20786	94	8	of	of	ADP
ajst-20786	94	9	alexnet	alexnet	NOUN
ajst-20786	94	10	,	,	PUNCT
ajst-20786	94	11	googlenet	googlenet	NOUN
ajst-20786	94	12	,	,	PUNCT
ajst-20786	94	13	and	and	CCONJ
ajst-20786	94	14	resnet-18	resnet-18	PROPN
ajst-20786	94	15	network	network	NOUN
ajst-20786	94	16	structures	structure	NOUN
ajst-20786	94	17	and	and	CCONJ
ajst-20786	94	18	tested	test	VERB
ajst-20786	94	19	the	the	DET
ajst-20786	94	20	accuracy	accuracy	NOUN
ajst-20786	94	21	of	of	ADP
ajst-20786	94	22	three	three	NUM
ajst-20786	94	23	network	network	NOUN
ajst-20786	94	24	classifications	classification	NOUN
ajst-20786	94	25	on	on	ADP
ajst-20786	94	26	dataset	dataset	NOUN
ajst-20786	94	27	fma	fma	NOUN
ajst-20786	94	28	-	-	PUNCT
ajst-20786	94	29	small	small	ADJ
ajst-20786	94	30	.	.	PUNCT
ajst-20786	95	1	the	the	DET
ajst-20786	95	2	results	result	NOUN
ajst-20786	95	3	were	be	AUX
ajst-20786	95	4	compared	compare	VERB
ajst-20786	95	5	and	and	CCONJ
ajst-20786	95	6	analyzed	analyze	VERB
ajst-20786	95	7	roughly	roughly	ADV
ajst-20786	95	8	.	.	PUNCT
ajst-20786	96	1	they	they	PRON
ajst-20786	96	2	provide	provide	VERB
ajst-20786	96	3	a	a	DET
ajst-20786	96	4	certain	certain	ADJ
ajst-20786	96	5	reference	reference	NOUN
ajst-20786	96	6	and	and	CCONJ
ajst-20786	96	7	guidance	guidance	NOUN
ajst-20786	96	8	direction	direction	NOUN
ajst-20786	96	9	for	for	ADP
ajst-20786	96	10	model	model	NOUN
ajst-20786	96	11	selection	selection	NOUN
ajst-20786	96	12	during	during	ADP
ajst-20786	96	13	amgc	amgc	NOUN
ajst-20786	96	14	execution	execution	NOUN
ajst-20786	96	15	and	and	CCONJ
ajst-20786	96	16	model	model	NOUN
ajst-20786	96	17	optimization	optimization	NOUN
ajst-20786	96	18	for	for	ADP
ajst-20786	96	19	small	small	ADJ
ajst-20786	96	20	dataset	dataset	NOUN
ajst-20786	96	21	classification	classification	NOUN
ajst-20786	96	22	.	.	PUNCT
ajst-20786	97	1	due	due	ADP
ajst-20786	97	2	to	to	ADP
ajst-20786	97	3	limitations	limitation	NOUN
ajst-20786	97	4	in	in	ADP
ajst-20786	97	5	time	time	NOUN
ajst-20786	97	6	and	and	CCONJ
ajst-20786	97	7	my	my	PRON
ajst-20786	97	8	own	own	ADJ
ajst-20786	97	9	abilities	ability	NOUN
ajst-20786	97	10	,	,	PUNCT
ajst-20786	97	11	although	although	SCONJ
ajst-20786	97	12	this	this	DET
ajst-20786	97	13	article	article	NOUN
ajst-20786	97	14	has	have	AUX
ajst-20786	97	15	obtained	obtain	VERB
ajst-20786	97	16	certain	certain	ADJ
ajst-20786	97	17	experimental	experimental	ADJ
ajst-20786	97	18	data	datum	NOUN
ajst-20786	97	19	and	and	CCONJ
ajst-20786	97	20	research	research	NOUN
ajst-20786	97	21	results	result	NOUN
ajst-20786	97	22	,	,	PUNCT
ajst-20786	97	23	there	there	PRON
ajst-20786	97	24	are	be	VERB
ajst-20786	97	25	still	still	ADV
ajst-20786	97	26	shortcomings	shortcoming	NOUN
ajst-20786	97	27	in	in	ADP
ajst-20786	97	28	experimental	experimental	ADJ
ajst-20786	97	29	design	design	NOUN
ajst-20786	97	30	and	and	CCONJ
ajst-20786	97	31	cause	cause	VERB
ajst-20786	97	32	analysis	analysis	NOUN
ajst-20786	97	33	.	.	PUNCT
ajst-20786	98	1	especially	especially	ADV
ajst-20786	98	2	in	in	ADP
ajst-20786	98	3	terms	term	NOUN
ajst-20786	98	4	of	of	ADP
ajst-20786	98	5	evaluation	evaluation	NOUN
ajst-20786	98	6	indicators	indicator	NOUN
ajst-20786	98	7	,	,	PUNCT
ajst-20786	98	8	control	control	NOUN
ajst-20786	98	9	of	of	ADP
ajst-20786	98	10	the	the	DET
ajst-20786	98	11	quantity	quantity	NOUN
ajst-20786	98	12	and	and	CCONJ
ajst-20786	98	13	quality	quality	NOUN
ajst-20786	98	14	of	of	ADP
ajst-20786	98	15	the	the	DET
ajst-20786	98	16	dataset	dataset	NOUN
ajst-20786	98	17	used	use	VERB
ajst-20786	98	18	,	,	PUNCT
ajst-20786	98	19	there	there	PRON
ajst-20786	98	20	is	be	VERB
ajst-20786	98	21	still	still	ADV
ajst-20786	98	22	room	room	NOUN
ajst-20786	98	23	for	for	ADP
ajst-20786	98	24	further	further	ADJ
ajst-20786	98	25	refinement	refinement	NOUN
ajst-20786	98	26	,	,	PUNCT
ajst-20786	98	27	which	which	PRON
ajst-20786	98	28	is	be	AUX
ajst-20786	98	29	also	also	ADV
ajst-20786	98	30	the	the	DET
ajst-20786	98	31	direction	direction	NOUN
ajst-20786	98	32	for	for	ADP
ajst-20786	98	33	further	further	ADJ
ajst-20786	98	34	improvement	improvement	NOUN
ajst-20786	98	35	by	by	ADP
ajst-20786	98	36	the	the	DET
ajst-20786	98	37	author	author	NOUN
ajst-20786	98	38	in	in	ADP
ajst-20786	98	39	the	the	DET
ajst-20786	98	40	future	future	NOUN
ajst-20786	98	41	.	.	PUNCT
ajst-20786	99	1	references	reference	NOUN
ajst-20786	99	2	[	[	X
ajst-20786	99	3	1	1	NUM
ajst-20786	99	4	]	]	X
ajst-20786	99	5	silla	silla	PROPN
ajst-20786	99	6	jr	jr	PROPN
ajst-20786	99	7	,	,	PUNCT
ajst-20786	99	8	c.	c.	PROPN
ajst-20786	99	9	n.	n.	PROPN
ajst-20786	99	10	,	,	PUNCT
ajst-20786	99	11	kaestner	kaestner	NOUN
ajst-20786	99	12	,	,	PUNCT
ajst-20786	99	13	c.	c.	PROPN
ajst-20786	99	14	a.	a.	PROPN
ajst-20786	99	15	,	,	PUNCT
ajst-20786	99	16	&	&	CCONJ
ajst-20786	99	17	koerich	koerich	PROPN
ajst-20786	99	18	,	,	PUNCT
ajst-20786	99	19	a.	a.	PROPN
ajst-20786	99	20	l.	l.	PROPN
ajst-20786	99	21	(	(	PUNCT
ajst-20786	99	22	2007	2007	NUM
ajst-20786	99	23	)	)	PUNCT
ajst-20786	99	24	.	.	PUNCT
ajst-20786	100	1	automatic	automatic	ADJ
ajst-20786	100	2	music	music	NOUN
ajst-20786	100	3	genre	genre	NOUN
ajst-20786	100	4	classification	classification	NOUN
ajst-20786	100	5	using	use	VERB
ajst-20786	100	6	ensemble	ensemble	NOUN
ajst-20786	100	7	of	of	ADP
ajst-20786	100	8	classifiers	classifier	NOUN
ajst-20786	100	9	.	.	PUNCT
ajst-20786	101	1	in2007	in2007	PROPN
ajst-20786	101	2	ieee	ieee	PROPN
ajst-20786	101	3	international	international	PROPN
ajst-20786	101	4	conference	conference	NOUN
ajst-20786	101	5	on	on	ADP
ajst-20786	101	6	systems	system	NOUN
ajst-20786	101	7	,	,	PUNCT
ajst-20786	101	8	man	man	NOUN
ajst-20786	101	9	and	and	CCONJ
ajst-20786	101	10	cybernetics	cybernetic	NOUN
ajst-20786	101	11	,	,	PUNCT
ajst-20786	101	12	pp	pp	ADP
ajst-20786	101	13	.	.	PUNCT
ajst-20786	101	14	1687	1687	NUM
ajst-20786	101	15	-	-	SYM
ajst-20786	101	16	1692	1692	NUM
ajst-20786	101	17	.	.	PUNCT
ajst-20786	102	1	[	[	X
ajst-20786	102	2	2	2	NUM
ajst-20786	102	3	]	]	PUNCT
ajst-20786	102	4	tzanetakis	tzanetakis	PROPN
ajst-20786	102	5	,	,	PUNCT
ajst-20786	102	6	g.	g.	PROPN
ajst-20786	102	7	,	,	PUNCT
ajst-20786	102	8	&	&	CCONJ
ajst-20786	102	9	cook	cook	VERB
ajst-20786	102	10	,	,	PUNCT
ajst-20786	102	11	p.	p.	NOUN
ajst-20786	102	12	(	(	PUNCT
ajst-20786	102	13	2002	2002	NUM
ajst-20786	102	14	)	)	PUNCT
ajst-20786	102	15	.	.	PUNCT
ajst-20786	103	1	musical	musical	ADJ
ajst-20786	103	2	genre	genre	NOUN
ajst-20786	103	3	classification	classification	NOUN
ajst-20786	103	4	of	of	ADP
ajst-20786	103	5	audio	audio	ADJ
ajst-20786	103	6	signals	signal	NOUN
ajst-20786	103	7	.	.	PUNCT
ajst-20786	104	1	ieee	ieee	NOUN
ajst-20786	104	2	transactions	transaction	NOUN
ajst-20786	104	3	on	on	ADP
ajst-20786	104	4	speech	speech	NOUN
ajst-20786	104	5	and	and	CCONJ
ajst-20786	104	6	audio	audio	NOUN
ajst-20786	104	7	processing	processing	NOUN
ajst-20786	104	8	,	,	PUNCT
ajst-20786	104	9	10(5	10(5	NUM
ajst-20786	104	10	):	):	PUNCT
ajst-20786	104	11	293	293	NUM
ajst-20786	104	12	-	-	SYM
ajst-20786	104	13	302	302	NUM
ajst-20786	104	14	.	.	PUNCT
ajst-20786	105	1	[	[	X
ajst-20786	105	2	3	3	NUM
ajst-20786	105	3	]	]	X
ajst-20786	105	4	kosina	kosina	NOUN
ajst-20786	105	5	,	,	PUNCT
ajst-20786	105	6	k.	k.	PROPN
ajst-20786	105	7	(	(	PUNCT
ajst-20786	105	8	2002	2002	NUM
ajst-20786	105	9	)	)	PUNCT
ajst-20786	105	10	.	.	PUNCT
ajst-20786	106	1	music	music	NOUN
ajst-20786	106	2	genre	genre	NOUN
ajst-20786	106	3	recognition	recognition	NOUN
ajst-20786	106	4	.	.	PUNCT
ajst-20786	107	1	[	[	X
ajst-20786	107	2	4	4	NUM
ajst-20786	107	3	]	]	X
ajst-20786	107	4	yan	yan	PROPN
ajst-20786	107	5	,	,	PUNCT
ajst-20786	107	6	j.	j.	PROPN
ajst-20786	107	7	(	(	PUNCT
ajst-20786	107	8	2022	2022	NUM
ajst-20786	107	9	)	)	PUNCT
ajst-20786	107	10	comparison	comparison	NOUN
ajst-20786	107	11	of	of	ADP
ajst-20786	107	12	machine	machine	NOUN
ajst-20786	107	13	learning	learning	NOUN
ajst-20786	107	14	and	and	CCONJ
ajst-20786	107	15	deep	deep	ADJ
ajst-20786	107	16	learning	learning	NOUN
ajst-20786	107	17	model	model	NOUN
ajst-20786	107	18	classification	classification	NOUN
ajst-20786	107	19	of	of	ADP
ajst-20786	107	20	music	music	NOUN
ajst-20786	107	21	genres	genre	NOUN
ajst-20786	107	22	.	.	PUNCT
ajst-20786	108	1	information	information	NOUN
ajst-20786	108	2	technology	technology	NOUN
ajst-20786	108	3	and	and	CCONJ
ajst-20786	108	4	informatization	informatization	NOUN
ajst-20786	108	5	,	,	PUNCT
ajst-20786	108	6	12:217	12:217	NUM
ajst-20786	108	7	-	-	SYM
ajst-20786	108	8	220	220	NUM
ajst-20786	108	9	.	.	PUNCT
ajst-20786	109	1	[	[	X
ajst-20786	109	2	5	5	NUM
ajst-20786	109	3	]	]	X
ajst-20786	109	4	gao	gao	PROPN
ajst-20786	109	5	,	,	PUNCT
ajst-20786	109	6	y.	y.	PROPN
ajst-20786	109	7	(	(	PUNCT
ajst-20786	109	8	2020	2020	NUM
ajst-20786	109	9	)	)	PUNCT
ajst-20786	109	10	research	research	NOUN
ajst-20786	109	11	on	on	ADP
ajst-20786	109	12	music	music	NOUN
ajst-20786	109	13	and	and	CCONJ
ajst-20786	109	14	audio	audio	NOUN
ajst-20786	109	15	classification	classification	NOUN
ajst-20786	109	16	based	base	VERB
ajst-20786	109	17	on	on	ADP
ajst-20786	109	18	deep	deep	ADJ
ajst-20786	109	19	learning	learning	NOUN
ajst-20786	109	20	.	.	PUNCT
ajst-20786	110	1	thesis	thesis	NOUN
ajst-20786	110	2	of	of	ADP
ajst-20786	110	3	south	south	PROPN
ajst-20786	110	4	china	china	PROPN
ajst-20786	110	5	university	university	PROPN
ajst-20786	110	6	of	of	ADP
ajst-20786	110	7	technology	technology	NOUN
ajst-20786	110	8	.	.	PUNCT
ajst-20786	111	1	[	[	X
ajst-20786	111	2	6	6	NUM
ajst-20786	111	3	]	]	SYM
ajst-20786	111	4	defferrard	defferrard	NOUN
ajst-20786	111	5	,	,	PUNCT
ajst-20786	111	6	m.	m.	NOUN
ajst-20786	111	7	,	,	PUNCT
ajst-20786	111	8	benzi	benzi	PROPN
ajst-20786	111	9	,	,	PUNCT
ajst-20786	111	10	k.	k.	PROPN
ajst-20786	111	11	,	,	PUNCT
ajst-20786	111	12	vandergheynst	vandergheynst	ADJ
ajst-20786	111	13	,	,	PUNCT
ajst-20786	111	14	p.	p.	NOUN
ajst-20786	111	15	,	,	PUNCT
ajst-20786	111	16	&	&	CCONJ
ajst-20786	111	17	bresson	bresson	PROPN
ajst-20786	111	18	,	,	PUNCT
ajst-20786	111	19	x.	x.	NOUN
ajst-20786	111	20	(	(	PUNCT
ajst-20786	111	21	2016	2016	NUM
ajst-20786	111	22	)	)	PUNCT
ajst-20786	111	23	.	.	PUNCT
ajst-20786	112	1	fma	fma	NOUN
ajst-20786	112	2	:	:	PUNCT
ajst-20786	112	3	a	a	DET
ajst-20786	112	4	dataset	dataset	NOUN
ajst-20786	112	5	for	for	ADP
ajst-20786	112	6	music	music	NOUN
ajst-20786	112	7	analysis	analysis	NOUN
ajst-20786	112	8	.	.	PUNCT
ajst-20786	113	1	arv	arv	NOUN
ajst-20786	113	2	preprint	preprint	VERB
ajst-20786	113	3	arv:1612.01840	arv:1612.01840	ADV
ajst-20786	113	4	.	.	PUNCT
ajst-20786	114	1	[	[	X
ajst-20786	114	2	7	7	NUM
ajst-20786	114	3	]	]	X
ajst-20786	114	4	krizhevsky	krizhevsky	NOUN
ajst-20786	114	5	,	,	PUNCT
ajst-20786	114	6	a.	a.	NOUN
ajst-20786	114	7	,	,	PUNCT
ajst-20786	114	8	sutskever	sutskever	PROPN
ajst-20786	114	9	,	,	PUNCT
ajst-20786	114	10	i.	i.	PROPN
ajst-20786	114	11	,	,	PUNCT
ajst-20786	114	12	&	&	CCONJ
ajst-20786	114	13	hinton	hinton	PROPN
ajst-20786	114	14	,	,	PUNCT
ajst-20786	114	15	g.	g.	PROPN
ajst-20786	114	16	e.	e.	PROPN
ajst-20786	114	17	(	(	PUNCT
ajst-20786	114	18	2017	2017	NUM
ajst-20786	114	19	)	)	PUNCT
ajst-20786	114	20	.	.	PUNCT
ajst-20786	115	1	imagenet	imagenet	PROPN
ajst-20786	115	2	classification	classification	NOUN
ajst-20786	115	3	with	with	ADP
ajst-20786	115	4	deep	deep	ADJ
ajst-20786	115	5	convolutional	convolutional	ADJ
ajst-20786	115	6	neural	neural	ADJ
ajst-20786	115	7	networks	network	NOUN
ajst-20786	115	8	.	.	PUNCT
ajst-20786	116	1	communications	communication	NOUN
ajst-20786	116	2	of	of	ADP
ajst-20786	116	3	the	the	DET
ajst-20786	116	4	acm	acm	NOUN
ajst-20786	116	5	,	,	PUNCT
ajst-20786	116	6	60(6	60(6	NOUN
ajst-20786	116	7	):	):	PUNCT
ajst-20786	116	8	84	84	NUM
ajst-20786	116	9	-	-	SYM
ajst-20786	116	10	90	90	NUM
ajst-20786	116	11	.	.	PUNCT
ajst-20786	116	12	78	78	NUM
ajst-20786	117	1	[	[	SYM
ajst-20786	117	2	8	8	NUM
ajst-20786	117	3	]	]	X
ajst-20786	117	4	attri	attri	PROPN
ajst-20786	117	5	,	,	PUNCT
ajst-20786	117	6	i.	i.	PROPN
ajst-20786	117	7	,	,	PUNCT
ajst-20786	117	8	awasthi	awasthi	PROPN
ajst-20786	117	9	,	,	PUNCT
ajst-20786	117	10	l.	l.	PROPN
ajst-20786	117	11	k.	k.	PROPN
ajst-20786	117	12	,	,	PUNCT
ajst-20786	117	13	sharma	sharma	PROPN
ajst-20786	117	14	,	,	PUNCT
ajst-20786	117	15	t.	t.	PROPN
ajst-20786	117	16	p.	p.	PROPN
ajst-20786	117	17	,	,	PUNCT
ajst-20786	117	18	&	&	CCONJ
ajst-20786	117	19	rathee	rathee	PROPN
ajst-20786	117	20	,	,	PUNCT
ajst-20786	117	21	p.	p.	NOUN
ajst-20786	117	22	(	(	PUNCT
ajst-20786	117	23	2023	2023	NUM
ajst-20786	117	24	)	)	PUNCT
ajst-20786	117	25	.	.	PUNCT
ajst-20786	118	1	a	a	DET
ajst-20786	118	2	review	review	NOUN
ajst-20786	118	3	of	of	ADP
ajst-20786	118	4	deep	deep	ADJ
ajst-20786	118	5	learning	learning	NOUN
ajst-20786	118	6	techniques	technique	NOUN
ajst-20786	118	7	used	use	VERB
ajst-20786	118	8	in	in	ADP
ajst-20786	118	9	agriculture	agriculture	NOUN
ajst-20786	118	10	.	.	PUNCT
ajst-20786	119	1	ecological	ecological	ADJ
ajst-20786	119	2	informatics	informatic	NOUN
ajst-20786	119	3	,	,	PUNCT
ajst-20786	119	4	102217	102217	NUM
ajst-20786	119	5	.	.	PUNCT
ajst-20786	120	1	[	[	X
ajst-20786	120	2	9	9	NUM
ajst-20786	120	3	]	]	X
ajst-20786	120	4	szegedy	szegedy	PROPN
ajst-20786	120	5	,	,	PUNCT
ajst-20786	120	6	c.	c.	PROPN
ajst-20786	120	7	,	,	PUNCT
ajst-20786	120	8	liu	liu	PROPN
ajst-20786	120	9	,	,	PUNCT
ajst-20786	120	10	w.	w.	PROPN
ajst-20786	120	11	,	,	PUNCT
ajst-20786	120	12	jia	jia	PROPN
ajst-20786	120	13	,	,	PUNCT
ajst-20786	120	14	y.	y.	PROPN
ajst-20786	120	15	,	,	PUNCT
ajst-20786	120	16	sermanet	sermanet	NOUN
ajst-20786	120	17	,	,	PUNCT
ajst-20786	120	18	p.	p.	PROPN
ajst-20786	120	19	,	,	PUNCT
ajst-20786	120	20	reed	reed	PROPN
ajst-20786	120	21	,	,	PUNCT
ajst-20786	120	22	s.	s.	PROPN
ajst-20786	120	23	,	,	PUNCT
ajst-20786	120	24	anguelov	anguelov	PROPN
ajst-20786	120	25	,	,	PUNCT
ajst-20786	120	26	d.	d.	PROPN
ajst-20786	120	27	,	,	PUNCT
ajst-20786	120	28	...	...	PUNCT
ajst-20786	120	29	&	&	CCONJ
ajst-20786	120	30	rabinovich	rabinovich	PROPN
ajst-20786	120	31	,	,	PUNCT
ajst-20786	120	32	a.	a.	PROPN
ajst-20786	120	33	(	(	PUNCT
ajst-20786	120	34	2015	2015	NUM
ajst-20786	120	35	)	)	PUNCT
ajst-20786	120	36	.	.	PUNCT
ajst-20786	121	1	going	go	VERB
ajst-20786	121	2	deeper	deeply	ADV
ajst-20786	121	3	with	with	ADP
ajst-20786	121	4	convolutions	convolution	NOUN
ajst-20786	121	5	.	.	PUNCT
ajst-20786	122	1	in	in	ADP
ajst-20786	122	2	proceedings	proceeding	NOUN
ajst-20786	122	3	of	of	ADP
ajst-20786	122	4	the	the	DET
ajst-20786	122	5	ieee	ieee	NOUN
ajst-20786	122	6	conference	conference	NOUN
ajst-20786	122	7	on	on	ADP
ajst-20786	122	8	computer	computer	NOUN
ajst-20786	122	9	vision	vision	NOUN
ajst-20786	122	10	and	and	CCONJ
ajst-20786	122	11	pattern	pattern	NOUN
ajst-20786	122	12	recognition	recognition	NOUN
ajst-20786	122	13	,	,	PUNCT
ajst-20786	122	14	pp	pp	PROPN
ajst-20786	122	15	.	.	PUNCT
ajst-20786	123	1	1	1	NUM
ajst-20786	123	2	-	-	SYM
ajst-20786	123	3	9	9	NUM
ajst-20786	123	4	.	.	PUNCT
ajst-20786	124	1	[	[	X
ajst-20786	124	2	10	10	NUM
ajst-20786	124	3	]	]	SYM
ajst-20786	124	4	simonyan	simonyan	ADJ
ajst-20786	124	5	,	,	PUNCT
ajst-20786	124	6	k.	k.	PROPN
ajst-20786	124	7	,	,	PUNCT
ajst-20786	124	8	&	&	CCONJ
ajst-20786	124	9	zisserman	zisserman	PROPN
ajst-20786	124	10	,	,	PUNCT
ajst-20786	124	11	a.	a.	NOUN
ajst-20786	124	12	(	(	PUNCT
ajst-20786	124	13	2014	2014	NUM
ajst-20786	124	14	)	)	PUNCT
ajst-20786	124	15	.	.	PUNCT
ajst-20786	125	1	very	very	ADV
ajst-20786	125	2	deep	deep	ADJ
ajst-20786	125	3	convolutional	convolutional	ADJ
ajst-20786	125	4	networks	network	NOUN
ajst-20786	125	5	for	for	ADP
ajst-20786	125	6	large	large	ADJ
ajst-20786	125	7	-	-	PUNCT
ajst-20786	125	8	scale	scale	NOUN
ajst-20786	125	9	image	image	NOUN
ajst-20786	125	10	recognition	recognition	NOUN
ajst-20786	125	11	.	.	PUNCT
ajst-20786	126	1	arv	arv	PROPN
ajst-20786	126	2	preprint	preprint	NOUN
ajst-20786	126	3	arv:1409.1556	arv:1409.1556	NOUN
ajst-20786	126	4	.	.	PUNCT
ajst-20786	127	1	[	[	X
ajst-20786	127	2	11	11	NUM
ajst-20786	127	3	]	]	X
ajst-20786	127	4	bae	bae	NOUN
ajst-20786	127	5	,	,	PUNCT
ajst-20786	127	6	w.	w.	PROPN
ajst-20786	127	7	,	,	PUNCT
ajst-20786	127	8	yoo	yoo	PROPN
ajst-20786	127	9	,	,	PUNCT
ajst-20786	127	10	j.	j.	PROPN
ajst-20786	127	11	,	,	PUNCT
ajst-20786	127	12	&	&	CCONJ
ajst-20786	127	13	chul	chul	PROPN
ajst-20786	127	14	ye	ye	PROPN
ajst-20786	127	15	,	,	PUNCT
ajst-20786	127	16	j.	j.	PROPN
ajst-20786	127	17	(	(	PUNCT
ajst-20786	127	18	2017	2017	NUM
ajst-20786	127	19	)	)	PUNCT
ajst-20786	127	20	.	.	PUNCT
ajst-20786	128	1	beyond	beyond	ADP
ajst-20786	128	2	deep	deep	ADJ
ajst-20786	128	3	residual	residual	ADJ
ajst-20786	128	4	learning	learning	NOUN
ajst-20786	128	5	for	for	ADP
ajst-20786	128	6	image	image	NOUN
ajst-20786	128	7	restoration	restoration	NOUN
ajst-20786	128	8	:	:	PUNCT
ajst-20786	128	9	persistent	persistent	ADJ
ajst-20786	128	10	homology	homology	NOUN
ajst-20786	128	11	-	-	PUNCT
ajst-20786	128	12	guided	guide	VERB
ajst-20786	128	13	manifold	manifold	ADJ
ajst-20786	128	14	simplification	simplification	NOUN
ajst-20786	128	15	.	.	PUNCT
ajst-20786	129	1	in	in	ADP
ajst-20786	129	2	proceedings	proceeding	NOUN
ajst-20786	129	3	of	of	ADP
ajst-20786	129	4	the	the	DET
ajst-20786	129	5	ieee	ieee	NOUN
ajst-20786	129	6	conference	conference	NOUN
ajst-20786	129	7	on	on	ADP
ajst-20786	129	8	computer	computer	NOUN
ajst-20786	129	9	vision	vision	NOUN
ajst-20786	129	10	and	and	CCONJ
ajst-20786	129	11	pattern	pattern	NOUN
ajst-20786	129	12	recognition	recognition	NOUN
ajst-20786	129	13	workshops	workshop	NOUN
ajst-20786	129	14	,	,	PUNCT
ajst-20786	129	15	pp	pp	ADJ
ajst-20786	129	16	.	.	PUNCT
ajst-20786	130	1	145	145	NUM
ajst-20786	130	2	-	-	SYM
ajst-20786	130	3	153	153	NUM
ajst-20786	130	4	.	.	PUNCT