Supervised nonnegative matrix factorization with Dual-Itakura-Saito and Kullback-Leibler divergences for music transcription

Hideaki Kagami, Masahiro Yukawa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    In this paper, we present a convex-analytic approach to supervised nonnegative matrix factorization (SNMF) based on the Dual-Itakura-Saito (Dual-IS) and Kullback-Leibler (KL) divergences for music transcription. The Dual-IS and KL divergences define convex fidelity functions, whereas the IS divergence defines a nonconvex one. The SNMF problem is formulated as minimizing the divergence-based fidelity function penalized by the ℓ1 and row-block ℓ1 norms subject to the nonnegativity constraint. Simulation results show that (i) the use of the Dual-IS and KL divergences yields better performance than the squared Euclidean distance and that (ii) the use of the Dual-IS divergence prevents from false alarms efficiently.

    Original languageEnglish
    Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
    PublisherEuropean Signal Processing Conference, EUSIPCO
    Pages1138-1142
    Number of pages5
    Volume2016-November
    ISBN (Electronic)9780992862657
    DOIs
    Publication statusPublished - 2016 Nov 28
    Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
    Duration: 2016 Aug 282016 Sep 2

    Other

    Other24th European Signal Processing Conference, EUSIPCO 2016
    CountryHungary
    CityBudapest
    Period16/8/2816/9/2

    ASJC Scopus subject areas

    • Signal Processing
    • Electrical and Electronic Engineering

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