Audio enhancement and intelligent classification of household sound events using a sparsely deployed array

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

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Original languageEnglish
Pages (from-to)11-24
Journal / PublicationJournal of the Acoustical Society of America
Volume147
Issue number1
Online published13 Jan 2020
Publication statusPublished - Jan 2020

Abstract

A household sound event classification system consisting of an audio localization and enhancement front-end cascaded with an intelligent classification back-end is presented. The front-end is composed of a sparsely deployed microphone array and a preprocessing unit to localize the source and extract the associated signal. In the front-end, a two-stage method and a direct method are compared for localization. The two-stage method introduces a subspace algorithm to estimate the time difference of arrival, followed by a constrained least squares algorithm to determine the source location. The direct localization methods, the delay-and-sum beamformer, the minimum power distortionless response beamformer, and the multiple signal classification algorithm are compared in terms of localization performance for sparse array configuration. A modified particle swarm optimization algorithm enabled an efficient grid-search. A minimum variance distortionless response beamformer in conjunction with a minimum-mean-square-error postfilter is exploited to extract the source signals for sound event classification tasks that follow. The back-end of the system is a sound event classifier that is based on convolutional neural networks (CNNs), and convolutional long short-term memory networks Mel-spectrograms are used as the input features to the CNNs. Simulations and experiments conducted in a live room have demonstrated the strength and weakness of the direct and two-stage methods. Signal quality enhancement using the array-based front-end proves beneficial for improved classification accuracy over a single microphone. © 2020 Acoustical Society of America.

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