Unsupervised Single-Channel Separation of Nonstationary Signals Using Gammatone Filterbank and Itakura–Saito Nonnegative Matrix Two-Dimensional Factorizations

  1. Lookup NU author(s)
  2. Dr Bin Gao
  3. Dr Wai Lok Woo
  4. Professor Satnam Dlay
Author(s)Gao B, Woo WL, Dlay SS
Publication type Article
JournalIEEE Transactions on Circuits and Systems I
ISSN (print)1932-4545
ISSN (electronic)1940-9990
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
A new unsupervised single-channel source separation method is presented. The proposed method does not require training knowledge and the separation system is based on nonuniform time-frequency (TF) analysis and feature extraction. Unlike conventional researches that concentrate on the use of spectrogram or its variants, we develop our separation algorithms using an alternative TF representation based on the gammatone filterbank. In particular, we show that the monaural mixed audio signal is considerably more separable in this nonuniform TF domain. We also provide the analysis of signal separability to verify this finding. In addition, we derive two new algorithms that extend the recently published Itakura-Saito nonnegative matrix factorization to the case of convolutive model for the nonstationary source signals. These formulations are based on the Quasi-EM framework and the multiplicative gradient descent (MGD) rule, respectively. Experimental tests have been conducted which show that the proposed method is efficient in extracting the sources' spectral-temporal features that are characterized by large dynamic range of energy, and thus leading to significant improvement in source separation performance.
Actions    Link to this publication

Altmetrics provided by Altmetric