Variational Bayesian model averaging for audio source separation

Xabier Jaureguiberry (1, 2) , Emmanuel Vincent (3) , Gael Richard (1, 2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 TSI - Département Traitement du Signal et des Images
3 PAROLE - Analysis, perception and recognition of speech

Non-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.

Mots clés: Variational Bayes; Non-negative Matrix Factorization; Model Averaging; Audio Source Separation

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Xabier Jaureguiberry, Emmanuel Vincent, Gael Richard. Variational Bayesian model averaging for audio source separation. SSP (IEEE Workshop on Statistical Signal Processing), Jun 2014, Gold Coast, Australia. pp.4. ⟨hal-00986909⟩