Supervised nonnegative matrix factorization using active-period-aware structured l1-norm for music transcription

Yu Morikawa, Masahiro Yukawa, Hisakazu Kikuchi

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

    Abstract

    An active-period-aware supervised nonnegative matrix factorization (NMF) approach for music transcription is proposed. Supervised NMF relies on a set of known spectrograms associated with all musical instruments that may possibly be involved with given music data; this is supported by the availability of large database of a variety of musical instruments. It is free from the source-number determination problem and this is a significant advantage over the unsupervised NMF approaches. The proposed approach is composed of three steps. Step 1: Apply the existing supervised NMF algorithm. Step 2: Estimate the 'active' periods (during which musical sounds are present) based on the outcomes of Step 1. Step 3: Optimize a refined cost function reflecting the estimate of active periods. The awareness of active periods leads to avoidance of the so-called octave-errors which is a central issue of the existing supervised NMF method. Simulation results show the efficacy of the proposed approach.1

    Original languageEnglish
    Title of host publication2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages14-18
    Number of pages5
    ISBN (Electronic)9789881476807
    DOIs
    Publication statusPublished - 2016 Feb 19
    Event2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 - Hong Kong, Hong Kong
    Duration: 2015 Dec 162015 Dec 19

    Other

    Other2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015
    CountryHong Kong
    CityHong Kong
    Period15/12/1615/12/19

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Modelling and Simulation
    • Signal Processing

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  • Cite this

    Morikawa, Y., Yukawa, M., & Kikuchi, H. (2016). Supervised nonnegative matrix factorization using active-period-aware structured l1-norm for music transcription. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2015 (pp. 14-18). [7415510] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2015.7415510