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Acoustic vector sensor based speech source separation with mixed Gaussian-laplacian distributions

Chen, X, Alinaghi, A, Wang, W and Zhong, X (2013) Acoustic vector sensor based speech source separation with mixed Gaussian-laplacian distributions In: 18th International Conference on Digital Signal Processing, 2013-07-01 - 2013-07-03, Fira.

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Abstract

Acoustic vector sensor (AVS) based convolutive blind source separation problem has been recently addressed under the framework of probabilistic time-frequency (T-F) masking, where both the DOA and the mixing vector cues are modelled by Gaussian distributions. In this paper, we show that the distributions of these cues vary with room acoustics, such as reverberation. Motivated by this observation, we propose a mixed model of Laplacian and Gaussian distributions to provide a better fit for these cues. The parameters of the mixed model are estimated and refined iteratively by an expectation-maximization (EM) algorithm. Experiments performed on the speech mixtures in simulated room environments show that the mixed model offers an average of about 0.68 dB and 1.18 dB improvements in signal-to-distotion (SDR) over the Gaussian and Laplacian model, respectively. © 2013 IEEE.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Chen, XUNSPECIFIEDUNSPECIFIED
Alinaghi, AUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Zhong, XUNSPECIFIEDUNSPECIFIED
Date : 2013
Identification Number : 10.1109/ICDSP.2013.6622676
Contributors :
ContributionNameEmailORCID
PublisherIEEE, UNSPECIFIEDUNSPECIFIED
Additional Information : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 30 Sep 2014 15:42
Last Modified : 01 Oct 2014 01:33
URI: http://epubs.surrey.ac.uk/id/eprint/806097

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