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

• Published in: 2017 IEEE International Conference on Multimedia and Expo (ICME) pp. 589 - 594
• Main Authors: Chien-Yao Wang, Santoso, Andri, Mathulaprangsan, Seksan, Chin-Chin Chiang, Chung-Hsien Wu, Jia-Ching Wang
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-07-2017
• Subjects: Atomic layer deposition; Convolutional codes; Dictionaries; Encoding; Hidden Markov models; sound event recognition; sound event retrieval; Sparse coding convolutional neural network; Spectrogram; Training
• ISSN: 1945-788X
• DOI: 10.1109/ICME.2017.8019552

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%.