Low Complex Simple Measurement Matrix for Sparse Recovery of Speech Signal
Article No.: 34, Pages 1 - 5
Abstract
Compressed Sensing (CS), the methodology of signal capturing, allows sampling at flexible rates below Nyquist, with the constraint that the sparsifying basis and the level of sparsity are known in advance for the signal of interest. Many speech codecs based on CS frame work are developed using Linear Predictive Coding (LPC), Discrete Cosine Transform (DCT) and Code Excited Linear Prediction (CELP). In most of them, Gaussian random matrix is used for deriving the observation vector which is computationally complex and has large memory requirements. In this paper, a modified binary sensing matrix, specifically for speech signal is proposed, which has low coherence with the sparsifying bases used for reconstruction. The Signal-to-Noise Ratio (SNR) improvement goes beyond 3-4 dB and it is more significant at very high compression ratios. The application of the proposed sensing matrix to CS based codecs using CELP and dynamic DCT&LPC bases shows significant improvement in the perceptual quality of the reconstructed speech. This enables the functioning of these codecs at lower bit rates without compromising the quality.
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Index Terms
- Low Complex Simple Measurement Matrix for Sparse Recovery of Speech Signal
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Published In
August 2018
402 pages
ISBN:9781450365291
DOI:10.1145/3271553
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Association for Computing Machinery
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Published: 27 August 2018
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ICVISP 2018
ICVISP 2018: The 2nd International Conference on Vision, Image and Signal Processing
August 27 - 29, 2018
NV, Las Vegas, USA
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