Multichannel audio source separation exploiting NMF-based generic source spectral model in gaussian modeling framework

Abstract

Nonnegative matrix factorization (NMF) has been well-known as a powerful spectral model for audio signals. Existing work, including ours, has investigated the use of generic source spectral models (GSSM) based on NMF for single-channel audio source separation and shown its efficiency in different settings. This paper extends the work to multichannel case where the GSSM is combined with the source spatial covariance model within a unified Gaussian modeling framework. Especially, unlike a conventional combination where the estimated variances of each source are further constrained by NMF separately, we propose to constrain the total variances of all sources altogether and found a better separation performance. We present the expectation-maximization (EM) algorithm for the parameter estimation. We demonstrate the effectiveness of the proposed approach by using a benchmark dataset provided within the 2016 Signal Separation Evaluation Campaign.