A comparative study on sparsity penalties for NMF-based speech separation: Beyond LP-norms

Abstract

In this work, we study the usefulness of several types of sparsity penalties in the task of speech separation using supervised and semi-supervised Nonnegative Matrix Factorization (NMF). We compare different criteria from the literature to two novel penalty functions based on Wiener Entropy, in a large-scale evaluation on spontaneous speech overlaid by realistic domestic noise, as well as music and stationary environmental noise corpora. The results show that enforcing the sparsity constraint in the separation phase does not improve the perceptual quality. In the learning phase however, it yields a better estimation of the base spectra, especially in the case of supervised NMF, where the proposed criteria delivered the best results.