Itakura-Saito nonnegative matrix factorization with group sparsity

We propose an unsupervised inference procedure for audio source separation. Components in nonnegative matrix factor-ization (NMF) are grouped automatically in audio sources via a penalized maximum likelihood approach. The penalty term we introduce favors sparsity at the group level, and is motivated by the assumption that the local amplitude of the sources are independent. Our algorithm extends multiplica-tive updates for NMF; moreover we propose a test statistic to tune hyperparameters in our model, and illustrate its adequacy on synthetic data. Results on real audio tracks show that our sparsity prior allows to identify audio sources without knowl-edge on their spectral properties. Index Terms — Blind source separation, audio signal pro-cessing, unsupervised learning, nonnegative matrix factoriza-tion, sparsity priors 1