Joint unsupervised learning of hidden Markov source models and source location models for multichannel source separation
Tomohiro Nakatani, Shoko Araki, Takuya Yoshioka, Masakiyo Fujimoto · 2011
This paper discusses a multichannel source separation approach that exploits the statistical characteristics of source location cues characterized by steering vector models (SM) and those of source log spectra characterized by hidden Markov models (spectral HMM). Recently, it was shown that the use of speaker independent spectral HMMs trained in advance substantially improves the quality of speech signals separated based on source location cues in a computationally efficient manner. However, with this approach, mismatches between the spectral HMMs and the observation may substantially degrade the separation quality, which limits the applicability of this approach. To overcome this problem, this paper proposes a method for learning the parameters of the spectral HMMs jointly with those of the SMs from the observed sound mixtures. Experimental results show that the proposed method works effectively for separation of convolutive sound mixtures.