Robust Environmental Sound Recognition with Sparse Key-Point Encoding and Efficient Multispike Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • Qiang Yu
  • Yanli Yao
  • Longbiao Wang
  • Huajin Tang
  • Jianwu Dang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number9044638
Pages (from-to)625-638
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number2
Online published23 Mar 2020
Publication statusPublished - Feb 2021

Abstract

The capability for environmental sound recognition (ESR) can determine the fitness of individuals in a way to avoid dangers or pursue opportunities when critical sound events occur. It still remains mysterious about the fundamental principles of biological systems that result in such a remarkable ability. Additionally, the practical importance of ESR has attracted an increasing amount of research attention, but the chaotic and nonstationary difficulties continue to make it a challenging task. In this article, we propose a spike-based framework from a more brain-like perspective for the ESR task. Our framework is a unifying system with consistent integration of three major functional parts which are sparse encoding, efficient learning, and robust readout. We first introduce a simple sparse encoding, where key points are used for feature representation, and demonstrate its generalization to both spike- and nonspike-based systems. Then, we evaluate the learning properties of different learning rules in detail with our contributions being added for improvements. Our results highlight the advantages of multispike learning, providing a selection reference for various spike-based developments. Finally, we combine the multispike readout with the other parts to form a system for ESR. Experimental results show that our framework performs the best as compared to other baseline approaches. In addition, we show that our spike-based framework has several advantageous characteristics including early decision making, small dataset acquiring, and ongoing dynamic processing. Our framework is the first attempt to apply the multispike characteristic of nervous neurons to ESR. The outstanding performance of our approach would potentially contribute to draw more research efforts to push the boundaries of spike-based paradigm to a new horizon.

Research Area(s)

  • Brain-like processing, feature extraction, multispike learning, neuromorphic computing, robust sound recognition, spike encoding, spiking neural networks (SNNs)

Citation Format(s)

Robust Environmental Sound Recognition with Sparse Key-Point Encoding and Efficient Multispike Learning. / Yu, Qiang; Yao, Yanli; Wang, Longbiao; Tang, Huajin; Dang, Jianwu; Tan, Kay Chen.

In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 2, 9044638, 02.2021, p. 625-638.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review