Separating Time-Frequency Sources from Time-Domain Convolutive Mixtures Using Non-negative Matrix Factorization

Simon Leglaive (1) , Roland Badeau (1) , Gael Richard (1)

1 Télécom ParisTech

This paper addresses the problem of under-determined audio source separation in multichannel reverberant mixtures. We target a semi- blind scenario assuming that the mixing filters are known. Source separation is performed from the time-domain mixture signals in order to accurately model the convolutive mixing process. The source signals are however modeled as latent variables in a time-frequency domain. In a previous paper we proposed to use the modified discrete cosine transform. The present paper generalizes the method to the use of the odd-frequency short-time Fourier transform. In this domain, the source coefficients are modeled as centered complex Gaussian random variables whose variances are structured by means of a non-negative matrix factorization model. The inference procedure relies on a variational expectation-maximization algorithm. In the experiments we discuss the choice of the source representation and we show that the proposed approach outperforms two methods from the literature.

Mots clés: Audio source separation; reverberant mixtures; non-negative matrix factorization; variational inference

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

Simon Leglaive, Roland Badeau, Gael Richard. Separating Time-Frequency Sources from Time-Domain Convolutive Mixtures Using Non-negative Matrix Factorization. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Oct 2017, New Paltz, New York, United States. ⟨hal-01548469⟩