Vocal separation by constrained non-negative matrix factorization

Eri Ochiai, Takanori Fujisawa, Masaaki Ikehara

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

    3 Citations (Scopus)

    Abstract

    The vocal separation is to separate vocal part and remove the accompaniment part from the mixed music data. Vocal part include many information, singer, lyric and emotion of the song. If we can extraction only the vocal part from the original sound from CD source, it can be applied to various applications. In this paper, we propose a new method to take out the natural vocal parts from mixed music by using non-negative matrix factorization (NMF). This NMF-based framework separates the harmonic, percussive, and vocal structures from the input signal. We impose the constraint into each component to enforce its feature such as harmonic or temporal continuity. In addition, we propose a framework utilizing the prior information in order to achieve the valid vocal separation over this mathematical procedure. The experiments over some vocal databases show the proposed framework has the superior separation performance compared to the conventional methods. Also by considering the characteristics of music, we intend to obtain high accuracy result.

    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.
    Pages480-483
    Number of pages4
    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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