QUANTIFYING DISCRIMINABILITY BETWEEN NMF BASES

Discriminative nonnegative matrix factorization (DNMF) has been investigated as a promising basis-learning method for monaural source separation. To the best of our knowledge, however, no good and sound discussion has been made on quantitative definition of discriminability and it is difficult to evaluate how discriminative DNMF is actually. This paper introduces a quantitative measure to calculate how discriminative two NMF bases are. From the viewpoint of our measure, we compare three basis-learning methods of plain NMF, DNMF, and minimum-volume (min-vol) NMF. Experimental results of monaural speech separation reveal that min-vol NMF actually learns as discriminative bases as DNMF and achieves the best separation performance. This is probably because min-vol NMF can learn the most compact basis possible that can cover training data.