Deep attention based music genre classification

doi:10.1016/j.neucom.2019.09.054

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Deep attention based music genre classification
	Yu, Yang 1; Luo, Sen 2; Liu, Shenglan 2; Qiao, Hong3 ; Liu, Yang 2; Feng, Lin 2
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-01-08
卷号	372 页码:84-91
通讯作者	Liu, Shenglan(liusl@mail.dlut.edu.cn)
摘要	As an important component of music information retrieval, music genre classification attracts great attentions these years. Benefitting from the outstanding performance of deep neural networks in computer vision, some researchers apply CNN on music genre classification tasks with audio spectrograms as input instead, which has similarities with RGB images. These methods are based on a latent assumption that spectrums with different temporal steps have equal importance. However, it goes against the theory of processing bottleneck in psychology as well as our observation from audio spectrograms. By considering the differences of spectrums, we propose a new model incorporating with attention mechanism based on Bidirectional Recurrent Neural Network. Furthermore, two attention-based models (serial attention and parallelized attention) are implemented in this paper. Comparing with serial attention, parallelized attention is more flexible and gets better results in our experiments. Especially, the CNN-based parallelized attention models with taking STFT spectrograms as input outperform the previous work. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Music genre classification Deep neural networks Serial attention Parallelized attention
DOI	10.1016/j.neucom.2019.09.054
关键词[WOS]	FEATURES ; NETWORKS
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of P. R. China[61672130] ; National Natural Science Foundation of P. R. China[61602082] ; National Key Research and Development Program of China[2017YFB130 020 0] ; National Key Research and Development Program of China[2017YFB130 020 0] ; National Natural Science Foundation of P. R. China[61602082] ; National Natural Science Foundation of P. R. China[61672130]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of P. R. China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000496135100009
出版者	ELSEVIER
引用统计	被引频次：42[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28893
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Liu, Shenglan
作者单位	1.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China 2.Dalian Univ Technol, Sch Innovat & Enterpreneurship, Dalian 116024, Peoples R China 3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yu, Yang,Luo, Sen,Liu, Shenglan,et al. Deep attention based music genre classification[J]. NEUROCOMPUTING,2020,372:84-91.
APA	Yu, Yang,Luo, Sen,Liu, Shenglan,Qiao, Hong,Liu, Yang,&Feng, Lin.(2020).Deep attention based music genre classification.NEUROCOMPUTING,372,84-91.
MLA	Yu, Yang,et al."Deep attention based music genre classification".NEUROCOMPUTING 372(2020):84-91.