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A Source Counting Method using Acoustic Vector Sensor based on Sparse Modeling of DOA Histogram

Chen, Yang, Wang, Wenwu, Wang, Zhe and Xia, Bingyin (2018) A Source Counting Method using Acoustic Vector Sensor based on Sparse Modeling of DOA Histogram IEEE Signal Processing Letters, 26 (1). pp. 69-73.

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Abstract

The number of sources present in a mixture is crucial information often assumed to be known or detected by source counting. The exiting methods for source counting in underdetermined blind speech separation (UBSS) suffer from the overlapping between sources with low W-disjoint orthogonality (WDO). To address this issue, we propose to fit the direction of arrival (DOA) histogram with multiple von-Mises density (VM) functions directly and form a sparse recovery problem, where all the source clusters and the sidelobes in the DOA histogram are fitted with VM functions of different spatial parameters. We also developed a formula to perform the source counting taking advantage of the values of the sparse source vector to reduce the influence of sidelobes. Experiments are carried out to evaluate the proposed source counting method and the results show that the proposed method outperforms two well-known baseline methods.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Chen, Yang
Wang, WenwuW.Wang@surrey.ac.uk
Wang, Zhe
Xia, Bingyin
Date : 5 November 2018
DOI : 10.1109/LSP.2018.2879547
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Histograms; Mathematical model; Direction-of-arrival estimation; Shape; Clustering algorithms; Estimation; Acoustics; Source counting; AVS; DOA histogram; OMP
Depositing User : Clive Harris
Date Deposited : 08 Nov 2018 08:40
Last Modified : 11 Dec 2018 11:55
URI: http://epubs.surrey.ac.uk/id/eprint/849840

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