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

Fingerprint

Blind source separation
Source separation
Bins
Masks

Cite this

Jafari, I., Togneri, R., & Nordholm, S. E. (2014). On the use of the Watson mixture model for clustering-based under-determined blind source separation. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (pp. 988-992). Singapore: International Speech Communication Association.
Jafari, I. ; Togneri, Roberto ; Nordholm, S.E. / On the use of the Watson mixture model for clustering-based under-determined blind source separation. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Singapore : International Speech Communication Association, 2014. pp. 988-992
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Jafari, I, Togneri, R & Nordholm, SE 2014, On the use of the Watson mixture model for clustering-based under-determined blind source separation. in Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. International Speech Communication Association, Singapore, pp. 988-992, 15th Annual Conference of the International Speech Communication Association, Singapore, 14/09/14.

On the use of the Watson mixture model for clustering-based under-determined blind source separation. / Jafari, I.; Togneri, Roberto; Nordholm, S.E.

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Singapore : International Speech Communication Association, 2014. p. 988-992.

Research output: Chapter in Book/Conference paperConference paper

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AB - 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.

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Jafari I, Togneri R, Nordholm SE. On the use of the Watson mixture model for clustering-based under-determined blind source separation. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH. Singapore: International Speech Communication Association. 2014. p. 988-992