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Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior
By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing cano...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472005/ https://www.ncbi.nlm.nih.gov/pubmed/32824410 http://dx.doi.org/10.3390/s20164609 |
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author | Jia, Xiaoli Liu, Peilin Jiang, Sumxin |
author_facet | Jia, Xiaoli Liu, Peilin Jiang, Sumxin |
author_sort | Jia, Xiaoli |
collection | PubMed |
description | By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. To solve this problem, we propose a method which combines the approximate message passing (AMP) and Markov chain that exploits the dependence between the modified discrete cosine transform (MDCT) coefficients of a speech signal. To reconstruct the speech signal from CS samples, a turbo framework, which alternately iterates AMP and belief propagation along the Markov chain, is utilized. In addtion, a constrain is set to the turbo iteration to prevent the new method from divergence. Extensive experiments show that, compared to other traditional CS methods, the new method achieves a higher signal-to-noise ratio, and a higher perceptual evaluation of speech quality (PESQ) score. At the same time, it maintaines a better similarity of the energy distribution to the original speech spectrogram. The new method also achieves a comparable speech enhancement effect to the state-of-the-art method. |
format | Online Article Text |
id | pubmed-7472005 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-74720052020-09-17 Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior Jia, Xiaoli Liu, Peilin Jiang, Sumxin Sensors (Basel) Article By means of compressive sampling (CS), a sparse signal can be efficiently recovered from its far fewer samples than that required by the Nyquist–Shannon sampling theorem. However, recovering a speech signal from its CS samples is a challenging problem, as it is not sparse enough on any existing canonical basis. To solve this problem, we propose a method which combines the approximate message passing (AMP) and Markov chain that exploits the dependence between the modified discrete cosine transform (MDCT) coefficients of a speech signal. To reconstruct the speech signal from CS samples, a turbo framework, which alternately iterates AMP and belief propagation along the Markov chain, is utilized. In addtion, a constrain is set to the turbo iteration to prevent the new method from divergence. Extensive experiments show that, compared to other traditional CS methods, the new method achieves a higher signal-to-noise ratio, and a higher perceptual evaluation of speech quality (PESQ) score. At the same time, it maintaines a better similarity of the energy distribution to the original speech spectrogram. The new method also achieves a comparable speech enhancement effect to the state-of-the-art method. MDPI 2020-08-17 /pmc/articles/PMC7472005/ /pubmed/32824410 http://dx.doi.org/10.3390/s20164609 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jia, Xiaoli Liu, Peilin Jiang, Sumxin Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior |
title | Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior |
title_full | Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior |
title_fullStr | Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior |
title_full_unstemmed | Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior |
title_short | Speech Compressive Sampling Using Approximate Message Passing and a Markov Chain Prior |
title_sort | speech compressive sampling using approximate message passing and a markov chain prior |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7472005/ https://www.ncbi.nlm.nih.gov/pubmed/32824410 http://dx.doi.org/10.3390/s20164609 |
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