13th Annual Conference of the International Speech Communication Association

Portland, OR, USA
September 9-13, 2012

Gaussian Mixture Gain Priors for Regularized Nonnegative Matrix Factorization in Single- Channel Source Separation

Emad M. Grais, Hakan Erdogan

Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli Tuzla, Istanbul, Turkey

We propose a new method to incorporate statistical priors on the solution of the nonnegative matrix factorization (NMF) for single-channel source separation (SCSS) applications. The Gaussian mixture model (GMM) is used as a log-normalized gain prior model for the NMF solution. The normalization makes the prior models energy independent. In NMF based SCSS, NMF is used to decompose the spectra of the observed mixed signal as a weighted linear combination of a set of trained basis vectors. In this work, the NMF decomposition weights are enforced to consider statistical prior information on the weight combination patterns that the trained basis vectors can jointly receive for each source in the observed mixed signal. The NMF solutions for the weights are encouraged to increase the log likelihood with the trained gain prior GMMs while reducing the NMF reconstruction error at the same time.

Index Terms: Nonnegative matrix factorization, single-channel source separation, Gaussian mixture models

Full Paper

Bibliographic reference.  Grais, Emad M. / Erdogan, Hakan (2012): "Gaussian mixture gain priors for regularized nonnegative matrix factorization in single- channel source separation", In INTERSPEECH-2012, 1520-1523.