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IDA
2009
Springer

Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation

14 years 1 months ago
Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation
Abstract. The underdetermined blind audio source separation problem is often addressed in the time-frequency domain by assuming that each time-frequency point is an independently distributed random variable. Other approaches which are not blind assume a more structured model, like the Spectral Gaussian Mixture Models (Spectral-GMMs), thus exploiting statistical diversity of audio sources in the separation process. However, in this last approach, Spectral-GMMs are supposed to be learned from some training signals. In this paper, we propose a new approach for learning Spectral-GMMs of the sources without the need of using training signals. The proposed blind method significantly outperforms state-of-the-art approaches on stereophonic instantaneous music mixtures.
Simon Arberet, Alexey Ozerov, Rémi Gribonva
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where IDA
Authors Simon Arberet, Alexey Ozerov, Rémi Gribonval, Frédéric Bimbot
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