Abstract
This article proposes two novel deep convolutional neural networks (CNN), which are called the sparse coding convolutional neural network (SC-CNN) and the multi-convolutional-channel SC-CNN (MSC-CNN), to address the sound event recognition and retrieval problem. Unlike the general framework of a CNN, in which the feature learning process is performed hierarchically, the proposed framework models the whole memorization process in the human brain, including encoding, storage, and recollection. In particular, the MSC-CNN is designed to recognize multiple sound events that occur simultaneously. The experimental results indicate that the proposed SC-CNN and MSC-CNN outperforms the state-of-the-art systems in sound event recognition and retrieval.
Original language | English |
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Article number | 8952659 |
Pages (from-to) | 1875-1887 |
Number of pages | 13 |
Journal | IEEE/ACM Transactions on Audio Speech and Language Processing |
Volume | 28 |
DOIs | |
Publication status | Published - 2020 |
All Science Journal Classification (ASJC) codes
- Computer Science (miscellaneous)
- Acoustics and Ultrasonics
- Computational Mathematics
- Electrical and Electronic Engineering