Toggle Main Menu Toggle Search

Open Access padlockePrints

Cochleagram-based audio pattern separation using two-dimensional non-negative matrix factorization with automatic sparsity adaptation

Lookup NU author(s): Bo Gao, Dr Wai Lok Woo, Dr Li Khor


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


An unsupervised single channel audio separation method from pattern recognition viewpoint is presented. The proposed method does not require training knowledge and the separation system is based on non-uniform time-frequency (TF) analysis and feature extraction. Unlike conventional research that concentrates on the use of spectrogram or its variants, the proposed separation algorithm uses an alternative TF representation based on the gammatone filterbank. In particular, the monaural mixed audio signal is shown to be considerably more separable in this non-uniform TF domain. The analysis of signal separability to verify this finding is provided. In addition, a variational Bayesian approach is derived to learn the sparsity parameters for optimizing the matrix factorization. Experimental tests have been conducted, which show that the extraction of the spectral dictionary and temporal codes is more efficient using sparsity learning and subsequently leads to better separation performance. (C) 2014 Acoustical Society of America.

Publication metadata

Author(s): Gao B, Woo WL, Khor LC

Publication type: Article

Publication status: Published

Journal: Journal of the Acoustical Society of America.

Year: 2014

Volume: 135

Issue: 3

Pages: 1171-1185

Print publication date: 01/03/2014

ISSN (print): 0001-4966

ISSN (electronic): 1520-8524

Publisher: Acoustical Society of America


DOI: 10.1121/1.4864294


Altmetrics provided by Altmetric