ISCA Archive Interspeech 2014
ISCA Archive Interspeech 2014

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

Ingrid Jafari, Roberto Togneri, Sven Nordholm

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.


doi: 10.21437/Interspeech.2014-260

Cite as: Jafari, I., Togneri, R., Nordholm, S. (2014) On the use of the Watson mixture model for clustering-based under-determined blind source separation. Proc. Interspeech 2014, 988-992, doi: 10.21437/Interspeech.2014-260

@inproceedings{jafari14_interspeech,
  author={Ingrid Jafari and Roberto Togneri and Sven Nordholm},
  title={{On the use of the Watson mixture model for clustering-based under-determined blind source separation}},
  year=2014,
  booktitle={Proc. Interspeech 2014},
  pages={988--992},
  doi={10.21437/Interspeech.2014-260},
  issn={2308-457X}
}