• DocumentCode
    394679
  • Title

    Musical instrument identification based on F0-dependent multivariate normal distribution

  • Author

    Kitahara, Tetsuro ; Goto, Masataka ; Okuno, Hiroshi G.

  • Author_Institution
    Dept. of Intelligence Sci. & Technol., Kyoto Univ., Japan
  • Volume
    5
  • fYear
    2003
  • fDate
    6-10 April 2003
  • Abstract
    The pitch dependency of timbres has not been fully exploited in musical instrument identification. In this paper, we present a method using an F0-dependent multivariate normal distribution of which mean is represented by a function of fundamental frequency (F0). This F0-dependent mean function represents the pitch dependency of each feature, while the F0-normalized covariance represents the nonpitch dependency. Musical instrument sounds are first analyzed by the F0-dependent multivariate normal distribution, and then identified by using the discriminant function based on the Bayes decision rule. Experimental results of identifying 6247 solo tones of 19 musical instruments by 10-fold cross validation showed that the proposed method improved the recognition rate at individual-instrument level from 75.73% to 79.73%, and the recognition rate at category level from 88.20% to 90.65%.
  • Keywords
    Bayes methods; audio signal processing; frequency estimation; musical instruments; normal distribution; pattern recognition; Bayes decision rule; F0-dependent mean function; F0-dependent multivariate normal distribution; F0-normalized covariance; discriminant function; fundamental frequency; musical instrument identification; nonpitch dependency; pitch dependency; recognition rate; timbre pitch dependency; Cepstral analysis; Frequency; Gaussian distribution; Humans; Image analysis; Informatics; Instruments; Music information retrieval; Timbre;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03). 2003 IEEE International Conference on
  • ISSN
    1520-6149
  • Print_ISBN
    0-7803-7663-3
  • Type

    conf

  • DOI
    10.1109/ICASSP.2003.1199996
  • Filename
    1199996