Discriminative Layered Nonnegative Matrix Factorization for Speech Separation

Chung-Chien Hsu, Tai-Shih Chi, Jen-Tzung Chien

This paper proposes a discriminative layered nonnegative matrix factorization (DL-NMF) for monaural speech separation. The standard NMF conducts the parts-based representation using a single-layer of bases which was recently upgraded to the layered NMF (L-NMF) where a tree of bases was estimated for multi-level or multi-aspect decomposition of a complex mixed signal. In this study, we develop the DL-NMF by extending the generative bases in L-NMF to the discriminative bases which are estimated according to a discriminative criterion. The discriminative criterion is conducted by optimizing the recovery of the mixed spectra from the separated spectra and minimizing the reconstruction errors between separated spectra and original source spectra. The experiments on single-channel speech separation show the superiority of DL-NMF to NMF and L-NMF in terms of the SDR, SIR and SAR measures.

DOI: 10.21437/Interspeech.2016-415

Cite as

Hsu, C., Chi, T., Chien, J. (2016) Discriminative Layered Nonnegative Matrix Factorization for Speech Separation. Proc. Interspeech 2016, 560-564.

author={Chung-Chien Hsu and Tai-Shih Chi and Jen-Tzung Chien},
title={Discriminative Layered Nonnegative Matrix Factorization for Speech Separation},
booktitle={Interspeech 2016},