Pitch Based Sound Classification

Andreas Brinch Nielsen, Lars Kai Hansen, U Kjems

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    693 Downloads (Pure)

    Abstract

    A sound classification model is presented that can classify signals into music, noise and speech. The model extracts the pitch of the signal using the harmonic product spectrum. Based on the pitch estimate and a pitch error measure, features are created and used in a probabilistic model with soft-max output function. Both linear and quadratic inputs are used. The model is trained on 2 hours of sound and tested on publicly available data. A test classification error below 0.05 with 1 s classification windows is achieved. Further more it is shown that linear input performs as well as a quadratic, and that even though classification gets marginally better, not much is achieved by increasing the window size beyond 1 s.
    Original languageEnglish
    Title of host publication2006 IEEE International Conference on Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings.
    Volume3
    PublisherIEEE
    Publication date2006
    ISBN (Print)1-4244-0469-X
    DOIs
    Publication statusPublished - 2006
    EventIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) - Toulouse, France
    Duration: 14 May 200619 May 2006

    Conference

    ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)
    CountryFrance
    CityToulouse
    Period14/05/200619/05/2006

    Bibliographical note

    Copyright: 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

    Fingerprint Dive into the research topics of 'Pitch Based Sound Classification'. Together they form a unique fingerprint.

    Cite this