Multiple-order non-negative matrix factorization for speech enhancement

International audienceAmongst the speech enhancement techniques, statistical models based on Non-negative Matrix Factorization (NMF) have received great attention. In a single channel configuration, NMF is used to describe the spectral content of both the speech and noise sources. As the number of components can have a crucial influence on separation quality, we here propose to investigate model order selection based on the variational Bayesian approximation to the marginal likelihood of models of different orders. To go further, we propose to use model averaging to combine several single-order NMFs and we show that a straightforward application of model averaging principles is inefficient as it turned out to be equivalent to model selection. We thus introduce a parameter to control the entropy of the model order distribution which makes the averaging effective. We also show that our probabilistic model nicely extends to a multiple-order NMF model where several NMFs are jointly estimated and averaged. Experiments are conducted on real data from the CHiME challenge and give an interesting insight on the entropic parameter and model order priors. Separation results are also promising as model averaging outperforms single-order model selection. Finally, our multiple-order NMF shows an interesting gain in computation time

Variational Bayesian model averaging for audio source separation

International audienceNon-negative Matrix Factorization (NMF) has become popular in audio source separation in order to design source-specific models. The number of components of the NMF is known to have a noticeable influence on separation quality. Many methods have thus been proposed to select the best order for a given task. To go further, we propose here to use model averaging. As existing techniques do not allow an effective averaging, we introduce a generative model in which the number of components is a random variable and we propose a modification to conventional variational Bayesian (VB) inference. Experimental results on synthetic data show promising results as our model leads to better separation results and is less computationally demanding than conventional VB model selection