A Spiking Neural Network Framework for Robust Sound Classification

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

14 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number836
Journal / PublicationFrontiers in Neuroscience
Volume12
Online published19 Nov 2018
Publication statusPublished - Nov 2018

Link(s)

Abstract

Environmental sounds form part of our daily life. With the advancement of deep learning models and the abundance of training data, the performance of automatic sound classification (ASC) systems has improved significantly in recent years. However, the high computational cost, hence high power consumption, remains a major hurdle for large-scale implementation of ASC systems on mobile and wearable devices. Motivated by the observations that humans are highly effective and consume little power whilst analyzing complex audio scenes, we propose a biologically plausible ASC framework, namely SOM-SNN. This framework uses the unsupervised self-organizing map (SOM) for representing frequency contents embedded within the acoustic signals, followed by an event-based spiking neural network (SNN) for spatiotemporal spiking pattern classification. We report experimental results on the RWCP environmental sound and TIDIGITS spoken digits datasets, which demonstrate competitive classification accuracies over other deep learning and SNN-based models. The SOM-SNN framework is also shown to be highly robust to corrupting noise after multi-condition training, whereby the model is trained with noise-corrupted sound samples. Moreover, we discover the early decision making capability of the proposed framework: an accurate classification can be made with an only partial presentation of the input.

Research Area(s)

  • spiking neural network, self-organizing map, automatic sound classification, maximum-margin Tempotron classifier, noise robust multi-condition training, HUMAN AUDITORY-CORTEX, SELF-ORGANIZING MAP, EVENT CLASSIFICATION, RECOGNITION, RETRIEVAL, FEATURES, SENSOR, NOISE

Citation Format(s)

A Spiking Neural Network Framework for Robust Sound Classification. / Wu, Jibin; Chua, Yansong; Zhang, Malu; Li, Haizhou; Tan, Kay Chen.

In: Frontiers in Neuroscience, Vol. 12, 836, 11.2018.

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

Download Statistics

No data available