Mixtures of Gamma Priors for Non-negative Matrix Factorization Based Speech Separation

This paper deals with audio source separation using supervised non-negative matrix factorization (NMF). We propose a prior model based on mixtures of Gamma distributions for each sound class, which hyperparameters are trained given a training corpus. This formulation allows adapting the spectral basis vectors of the sound sources during actual operation, when the exact characteristics of the sources are not known in advance. Simulations were conducted using a random mixture of two speakers. Even without adaptation the mixture model outperformed the basic NMF, and adaptation furher improved slightly the separation quality. Audio demonstrations are available at www.cs.tut.fi/(similar to)tuomasv.