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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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%.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Multimedia and Expo, ICME 2017
PublisherIEEE Computer Society
Pages589-594
Number of pages6
ISBN (Electronic)9781509060672
DOIs
Publication statusPublished - 2017 Aug 28
Event2017 IEEE International Conference on Multimedia and Expo, ICME 2017 - Hong Kong, Hong Kong
Duration: 2017 Jul 102017 Jul 14

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2017 IEEE International Conference on Multimedia and Expo, ICME 2017
CountryHong Kong
CityHong Kong
Period17-07-1017-07-14

Fingerprint

Acoustic waves
Neural networks
Acoustic noise
Brain

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Wang, C. Y., Santoso, A., Mathulaprangsan, S., Chiang, C. C., Wu, C-H., & Wang, J. C. (2017). Recognition and retrieval of sound events using sparse coding convolutional neural network. In 2017 IEEE International Conference on Multimedia and Expo, ICME 2017 (pp. 589-594). [8019552] (Proceedings - IEEE International Conference on Multimedia and Expo). IEEE Computer Society. https://doi.org/10.1109/ICME.2017.8019552
Wang, Chien Yao ; Santoso, Andri ; Mathulaprangsan, Seksan ; Chiang, Chin Chin ; Wu, Chung-Hsien ; Wang, Jia Ching. / Recognition and retrieval of sound events using sparse coding convolutional neural network. 2017 IEEE International Conference on Multimedia and Expo, ICME 2017. IEEE Computer Society, 2017. pp. 589-594 (Proceedings - IEEE International Conference on Multimedia and Expo).
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title = "Recognition and retrieval of sound events using sparse coding convolutional neural network",
abstract = "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{\%}.",
author = "Wang, {Chien Yao} and Andri Santoso and Seksan Mathulaprangsan and Chiang, {Chin Chin} and Chung-Hsien Wu and Wang, {Jia Ching}",
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Wang, CY, Santoso, A, Mathulaprangsan, S, Chiang, CC, Wu, C-H & Wang, JC 2017, Recognition and retrieval of sound events using sparse coding convolutional neural network. in 2017 IEEE International Conference on Multimedia and Expo, ICME 2017., 8019552, Proceedings - IEEE International Conference on Multimedia and Expo, IEEE Computer Society, pp. 589-594, 2017 IEEE International Conference on Multimedia and Expo, ICME 2017, Hong Kong, Hong Kong, 17-07-10. https://doi.org/10.1109/ICME.2017.8019552

Recognition and retrieval of sound events using sparse coding convolutional neural network. / Wang, Chien Yao; Santoso, Andri; Mathulaprangsan, Seksan; Chiang, Chin Chin; Wu, Chung-Hsien; Wang, Jia Ching.

2017 IEEE International Conference on Multimedia and Expo, ICME 2017. IEEE Computer Society, 2017. p. 589-594 8019552 (Proceedings - IEEE International Conference on Multimedia and Expo).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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%.

AB - 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%.

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Wang CY, Santoso A, Mathulaprangsan S, Chiang CC, Wu C-H, Wang JC. Recognition and retrieval of sound events using sparse coding convolutional neural network. In 2017 IEEE International Conference on Multimedia and Expo, ICME 2017. IEEE Computer Society. 2017. p. 589-594. 8019552. (Proceedings - IEEE International Conference on Multimedia and Expo). https://doi.org/10.1109/ICME.2017.8019552