Recognition and retrieval of sound events using sparse coding convolutional neural network

Chien Yao Wang, Andri Santoso, Seksan Mathulaprangsan, Chin Chin Chiang, Chung Hsien Wu, Jia Ching Wang

研究成果: Conference contribution

9 引文 斯高帕斯(Scopus)

摘要

This paper proposes a novel deep convolutional neural network (CNN), called sparse coding convolutional neural network (SC-CNN), to address the problem of sound event recognition and retrieval task. Unlike the general framework of a CNN, in which feature learning process is performed hierarchically, the proposed framework models the whole memorizing procedures in the human brain, including encoding, storage, and recollection. Sound data from the RWCP sound scene dataset with added noise from NOISEX-92 noise dataset are used to compare the performance of the proposed system with the state-of-the-art baselines. The experimental results indicated that the proposed SC-CNN outperformed the state-of-the-art systems in sound event recognition and retrieval. In the sound event recognition task, the proposed system achieved an accuracy of 94.6%, 100% and 100% under 0db, 10db and clean noise conditions, respectively. In the retrieval task, the proposed system improves the mAP rate of the general CNN by approximately 6%.

原文English
主出版物標題2017 IEEE International Conference on Multimedia and Expo, ICME 2017
發行者IEEE Computer Society
頁面589-594
頁數6
ISBN(電子)9781509060672
DOIs
出版狀態Published - 2017 8月 28
事件2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
持續時間: 2017 7月 102017 7月 14

出版系列

名字Proceedings - IEEE International Conference on Multimedia and Expo
ISSN(列印)1945-7871
ISSN(電子)1945-788X

Other

Other2017 IEEE International Conference on Multimedia and Expo, ICME 2017
國家/地區Hong Kong
城市Hong Kong
期間17-07-1017-07-14

All Science Journal Classification (ASJC) codes

  • 電腦網路與通信
  • 電腦科學應用

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