On the use of the Watson mixture model for clustering-based under-determined blind source separation

I. Jafari, Roberto Togneri, S.E. Nordholm

Research output: Chapter in Book/Conference paperConference paper

5 Citations (Scopus)

Abstract

Copyright © 2014 ISCA. In this paper, we investigate the application of a generative clustering technique for the estimation of time-frequency source separation masks. Recent advances in time-frequency clustering-based approaches to blind source separation have touched upon the Watson mixture model (WMM) as a tool for source separation. However, most methods have been frequency bin-wise and have thus required the additional permutation alignment stage, and previous full-band methods which employ the WMM have yet to be applied to the under-determined setting. We propose to evaluate the clustering ability of the WMM within the clustering-based source separation framework. Evaluations confirm the superiority of the WMM against other previously used clustering techniques such as the fuzzy c-means.
Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Place of PublicationSingapore
PublisherInternational Speech Communication Association
Pages988-992
ISBN (Print)2308457X
Publication statusPublished - 2014
Event15th Annual Conference of the International Speech Communication Association - , Singapore
Duration: 14 Sep 201418 Sep 2014

Conference

Conference15th Annual Conference of the International Speech Communication Association
CountrySingapore
Period14/09/1418/09/14

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