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Hyper-parameterization of sparse reconstruction for speech enhancement

Hyper-parameterization of sparse reconstruction for speech enhancement
Hyper-parameterization of sparse reconstruction for speech enhancement

The regularized least squares for sparse reconstruction is gaining popularity as it has the ability to reconstruct speech signal from a noisy observation. The reconstruction relies on the sparsity of speech, which provides the demarcation from noise. However, there is no measure incorporated in the sparse reconstruction to optimize on the overall speech quality. This paper proposes a two-level optimization strategy to incorporate the quality design attributes in the sparse solution in compressive speech enhancement by hyper-parameterizing the tuning parameter. The first level involves the compression of the big data and the second level optimizes the tuning parameter by using different optimization criteria (such as Gini index, the Akaike information criterion (AIC) and Bayesian information criterion (BIC)). The set of solutions can then be measured against the desired design attributes to achieve the best trade-off between suppression and distortion. Numerical results show the proposed approach can effectively fuse the trade-offs in the solutions for different noise profile in a wide range of signal to noise ratios (SNR).

Compressed sensing, Regularized least squares, Speech enhancement
0003-682X
72-79
Shi, Yue
acbdf210-9ffa-4dfa-97fb-95da3be9e404
Low, Siow Yong
d101f0b9-404e-4e2a-bb4f-a605f0811108
Cedric Yiu, Ka Fai
f8fb295f-ec60-4ab5-9f86-ba719f4c4d3c
Shi, Yue
acbdf210-9ffa-4dfa-97fb-95da3be9e404
Low, Siow Yong
d101f0b9-404e-4e2a-bb4f-a605f0811108
Cedric Yiu, Ka Fai
f8fb295f-ec60-4ab5-9f86-ba719f4c4d3c

Shi, Yue, Low, Siow Yong and Cedric Yiu, Ka Fai (2018) Hyper-parameterization of sparse reconstruction for speech enhancement. Applied Acoustics, 138, 72-79. (doi:10.1016/j.apacoust.2018.03.020).

Record type: Article

Abstract

The regularized least squares for sparse reconstruction is gaining popularity as it has the ability to reconstruct speech signal from a noisy observation. The reconstruction relies on the sparsity of speech, which provides the demarcation from noise. However, there is no measure incorporated in the sparse reconstruction to optimize on the overall speech quality. This paper proposes a two-level optimization strategy to incorporate the quality design attributes in the sparse solution in compressive speech enhancement by hyper-parameterizing the tuning parameter. The first level involves the compression of the big data and the second level optimizes the tuning parameter by using different optimization criteria (such as Gini index, the Akaike information criterion (AIC) and Bayesian information criterion (BIC)). The set of solutions can then be measured against the desired design attributes to achieve the best trade-off between suppression and distortion. Numerical results show the proposed approach can effectively fuse the trade-offs in the solutions for different noise profile in a wide range of signal to noise ratios (SNR).

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More information

Accepted/In Press date: 19 March 2018
e-pub ahead of print date: 30 March 2018
Published date: 1 September 2018
Keywords: Compressed sensing, Regularized least squares, Speech enhancement

Identifiers

Local EPrints ID: 426572
URI: http://eprints.soton.ac.uk/id/eprint/426572
ISSN: 0003-682X
PURE UUID: 856bfcd1-fb89-435c-93b5-ba50ae1a5e06

Catalogue record

Date deposited: 30 Nov 2018 17:30
Last modified: 07 Oct 2020 00:14

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