Sound Events Recognition and Retrieval Using Multi-Convolutional-Channel Sparse Coding Convolutional Neural Networks

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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 languageEnglish
Article number8952659
Pages (from-to)1875-1887
Number of pages13
JournalIEEE/ACM Transactions on Audio Speech and Language Processing
Volume28
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Acoustics and Ultrasonics
  • Computational Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Sound Events Recognition and Retrieval Using Multi-Convolutional-Channel Sparse Coding Convolutional Neural Networks'. Together they form a unique fingerprint.

Cite this