15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

On the Use of the Watson Mixture Model for Clustering-Based Under-Determined Blind Source Separation

Ingrid Jafari (1), Roberto Togneri (1), Sven Nordholm (2)

(1) University of Western Australia, Australia
(2) Curtin University of Technology, Australia

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.

Full Paper

Bibliographic reference.  Jafari, Ingrid / Togneri, Roberto / Nordholm, Sven (2014): "On the use of the Watson mixture model for clustering-based under-determined blind source separation", In INTERSPEECH-2014, 988-992.