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

    Access Status
    Fulltext not available
    Authors
    Jafari, I.
    Togneri, R.
    Nordholm, Sven
    Date
    2014
    Type
    Conference Paper
    
    Metadata
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    Citation
    Jafari, I. and Togneri, R. and Nordholm, S. 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.
    Source Title
    Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
    ISSN
    2308-457X
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/26988
    Collection
    • Curtin Research Publications
    Abstract

    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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