Generalization of Supervised Learning for Binary Mask Estimation

Tobias May, Timo Gerkmann

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    This paper addresses the problem of speech segregation by es- timating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distri- bution observed during training. The second method solely relies on a frame-based speech presence probability (SPP) es- timation, and therefore, does not depend on the acoustic con- dition seen during training. We investigate the influence of mismatches between the acoustic conditions used for training and testing on the IBM estimation performance and discuss the advantages of both approaches.
    Original languageEnglish
    Title of host publicationProceedings of IWAENC 2014
    Number of pages5
    Publication date2014
    ISBN (Print)9781479968084
    Publication statusPublished - 2014
    EventThe International Workshop on Acoustic Signal Enhancement - Juan les Pins, France
    Duration: 8 Sept 201411 Sept 2014


    ConferenceThe International Workshop on Acoustic Signal Enhancement
    CityJuan les Pins


    • Ideal binary mask
    • Speech segregation
    • Generalization
    • Speech presence probability


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