Compressive speech enhancement
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This paper presents an alternative approach to speech enhancement by using compressed sensing (CS). CS is a new sampling theory, which states that sparse signals can be reconstructed from far fewer measurements than the Nyquist sampling. As such, CS can be exploited to reconstruct only the sparse components (e.g., speech) from the mixture of sparse and non-sparse components (e.g., noise). This is possible because in a time-frequency representation, speech signal is sparse whilst most noise is non-sparse. Derivation shows that on average the signal to noise ratio (SNR) in the compressed domain is greater or equal than the uncompressed domain. Experimental results concur with the derivation and the proposed CS scheme achieves better or similar perceptual evaluation of speech quality (PESQ) scores and segmental SNR compared to other conventional methods in a wide range of input SNR.
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Pham, DucSon (2015)Following advances in compressed sensing and high-dimensional statistics, many pattern recognition methods have been developed with l1 regularization, which promotes sparse solutions. In this work, we instead advocate ...
Pham, DucSon (2015)© 2015 Elsevier Ltd. All rights reserved. Following advances in compressed sensing and high-dimensional statistics, many pattern recognition methods have been developed with l1 regularization, which promotes sparse ...
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