A blind separation of monaural sound based on peak tracking of frequency spectra

Shoko Yamahata, Mitsuharu Matsumoto, Shuji Hashimoto

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

    1 Citation (Scopus)

    Abstract

    This paper describes a blind separation algorithm of monaural sound based on peak tracking of frequency spectra. We have already reported a blind separation method based on the change ratio of frequency components. However it cannot handle a signal with frequency fluctuation such as a speech signal or a vibrato tone, because such type of signal is regarded as the mixture of different sounds. Our new method proposed in this paper can handle a sound with frequency fluctuation by tracking frequency peaks along time axis. The effectiveness of the proposed method is evaluated with some experiments on real voice data.

    Original languageEnglish
    Title of host publicationProceedings - 2009 International Conference on Information Management and Engineering, ICIME 2009
    Pages305-311
    Number of pages7
    DOIs
    Publication statusPublished - 2009
    Event2009 International Conference on Information Management and Engineering, ICIME 2009 - Kuala Lumpur
    Duration: 2009 Apr 32009 Apr 5

    Other

    Other2009 International Conference on Information Management and Engineering, ICIME 2009
    CityKuala Lumpur
    Period09/4/309/4/5

    ASJC Scopus subject areas

    • Computer Science Applications
    • Information Systems
    • Information Systems and Management

    Fingerprint Dive into the research topics of 'A blind separation of monaural sound based on peak tracking of frequency spectra'. Together they form a unique fingerprint.

  • Cite this

    Yamahata, S., Matsumoto, M., & Hashimoto, S. (2009). A blind separation of monaural sound based on peak tracking of frequency spectra. In Proceedings - 2009 International Conference on Information Management and Engineering, ICIME 2009 (pp. 305-311). [5077048] https://doi.org/10.1109/ICIME.2009.33