Linear Regression on Sparse Features for Single-Channel Speech Separation

Mikkel N. Schmidt, Rasmus Kongsgaard Olsson

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    In this work we address the problem of separating multiple speakers from a single microphone recording. We formulate a linear regression model for estimating each speaker based on features derived from the mixture. The employed feature representation is a sparse, non-negative encoding of the speech mixture in terms of pre-learned speaker-dependent dictionaries. Previous work has shown that this feature representation by itself provides some degree of separation. We show that the performance is significantly improved when regression analysis is performed on the sparse, non-negative features, both compared to linear regression on spectral features and compared to separation based directly on the non-negative sparse features.
    Original languageEnglish
    Title of host publicationApplications of Signal Processing to Audio and Acoustics : IEEE Workshop on (WASPAA)
    Publication date2007
    ISBN (Print)978-1-4244-1620-2
    Publication statusPublished - 2007
    EventApplications of Signal Processing to Audio and Acoustics, IEEE Workshop on (WASPAA) - New Paltz, NY, USA
    Duration: 1 Jan 2007 → …


    ConferenceApplications of Signal Processing to Audio and Acoustics, IEEE Workshop on (WASPAA)
    CityNew Paltz, NY, USA
    Period01/01/2007 → …

    Bibliographical note

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