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    Compressive speech enhancement

    Access Status
    Fulltext not available
    Authors
    Low, S.
    Pham, DucSon
    Venkatesh, S.
    Date
    2013
    Type
    Journal Article
    
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    Abstract

    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.

    Citation
    Low, Siow Yong and Pham, Duc Son and Venkatesh, Svetha. 2013. Compressive speech enhancement. Speech Communication. 55 (6): pp. 757-768.
    Source Title
    Speech Communication
    DOI
    10.1016/j.specom.2013.03.003
    ISSN
    0167-6393
    School
    Sarawak Campus, Miri, Malaysia
    URI
    http://hdl.handle.net/20.500.11937/35000
    Collection
    • Curtin Research Publications

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