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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-08, Vol.20 (16), p.4609
Main Authors: Jia, Xiaoli, Liu, Peilin, Jiang, Sumxin
Format: Article
Language:English
Subjects:
Algorithms
approximate message passing
compressive sampling
Data Compression
Dictionaries
Discrete cosine transform
Markov analysis
Markov chain
Markov Chains
MDCT
Methods
Noise
Sampling
Signal to noise ratio
Speech
Speech processing
speech spectrogram
Variables
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20164609