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Sparse Blind Speech Deconvolution with Dynamic Range Regularization and Indicator Function

Guan, J, Wang, X, Wang, Wenwu and Huang, L (2017) Sparse Blind Speech Deconvolution with Dynamic Range Regularization and Indicator Function Circuits Systems and Signal Processing. pp. 1-16.

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Abstract

Blind deconvolution is an ill-posed problem. To solve such a prob- lem, prior information, such as, the sparseness of the source (i.e. input) signal or channel impulse responses, is usually adopted. In speech deconvolution, the source signal is not naturally sparse. However, the direct impulse and early reflections of the impulse responses of an acoustic system can be considered as sparse. In this paper, we exploit the channel sparsity and present an algorithm for speech deconvolution, where the dynamic range of the convolutive speech is also used as the prior information. In this algorithm, the estimation of the impulse response and the source signal is achieved by alternating between two steps, namely, the ℓ1 regularized least squares optimization and a proximal operation. As demonstrated in our experiments, the proposed method pro- vides superior performance for deconvolution of a sparse acoustic system, as compared with two state-of-the-art methods.

Item Type: Article
Subjects : Electronic Engineering
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Guan, JUNSPECIFIEDUNSPECIFIED
Wang, XUNSPECIFIEDUNSPECIFIED
Wang, WenwuW.Wang@surrey.ac.ukUNSPECIFIED
Huang, LUNSPECIFIEDUNSPECIFIED
Date : 6 February 2017
Identification Number : 10.1007/s00034-017-0505-x
Copyright Disclaimer : The final publication is available at Springer via http://dx.doi.org/10.1007/s00034-017-0505-x
Uncontrolled Keywords : Sparse blind deconvolution · ℓ1 regularized least squares · Speech deconvolution · Sparse channel estimation
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 15 Feb 2017 09:07
Last Modified : 07 Jul 2017 08:30
URI: http://epubs.surrey.ac.uk/id/eprint/813544

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