Deep attention based music genre classification
文献类型：期刊论文
| 作者 | Yu, Yang3 ; Luo, Sen2; Liu, Shenglan2; Qiao, Hong1; Liu, Yang2; Feng, Lin2
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-01-08 |
| 卷号 | 372页码:84-91 |
| 关键词 | Music genre classification Deep neural networks Serial attention Parallelized attention |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.09.054 |
| 通讯作者 | Liu, Shenglan(liusl@mail.dlut.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | FEATURES ; NETWORKS |
| 资助项目 | 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] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000496135100009 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of P. R. China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28892]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组 |
| 通讯作者 | Liu, Shenglan |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Dalian Univ Technol, Sch Innovat & Enterpreneurship, Dalian 116024, Peoples R China 3.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, 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. |
入库方式： OAI收割
来源：自动化研究所
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