Reduction of Non-stationary Noise using a Non-negative Latent Variable Decomposition

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    Abstract

    We present a method for suppression of non-stationary noise in single channel recordings of speech. The method is based on a non-negative latent variable decomposition model for the speech and noise signals, learned directly from a noisy mixture. In non-speech regions an over complete basis is learned for the noise that is then used to jointly estimate the speech and the noise from the mixture. We compare the method to the classical spectral subtraction approach, where the noise spectrum is estimated as the average over non-speech frames. The proposed method significantly outperforms the classic approach, especially when the noise is highly non-stationary and at low signal-to-noise ratios.
    Original languageEnglish
    Title of host publicationMachine Learning for Signal Processing, IEEE Workshop on
    PublisherIEEE
    Publication date2008
    ISBN (Print)978-1-4244-2375-0
    DOIs
    Publication statusPublished - 2008
    Event2008 IEEE International Workshop on Machine Learning for Signal Processing - Cancún, Mexico
    Duration: 16 Oct 200819 Oct 2008
    http://mlsp2008.conwiz.dk/

    Workshop

    Workshop2008 IEEE International Workshop on Machine Learning for Signal Processing
    CountryMexico
    CityCancún
    Period16/10/200819/10/2008
    Internet address

    Bibliographical note

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