• DocumentCode
    2802710
  • Title

    Generative model based polyphonic music transcription

  • Author

    Cemgil, Ali Taylan ; Kappen, Bert ; Barber, David

  • Author_Institution
    SNN, Nijmegen Univ., Netherlands
  • fYear
    2003
  • fDate
    19-22 Oct. 2003
  • Firstpage
    181
  • Lastpage
    184
  • Abstract
    We present a model for simultaneous tempo and polyphonic pitch tracking. Our model, a form of dynamic Bayesian network (Murphy, K.P., 2002), embodies a transparent and computationally tractable approach to this acoustic analysis problem. An advantage of our approach is that it places emphasis on modeling the sound generation procedure. It provides a clear framework in which both high level (cognitive) prior information on music structure can be coupled with low level (acoustic physical) information in a principled manner to perform the analysis. The model is readily extensible to more complex sound generation processes.
  • Keywords
    acoustic signal processing; audio signal processing; belief networks; music; acoustic analysis; acoustic physical information; acoustic signals; audio to piano-roll conversion; cognitive information; dynamic Bayesian network; generative model; polyphonic music transcription; polyphonic pitch tracking; signal reconstruction; signal representation; sound generation; sound generation procedure; tempo tracking; Bayesian methods; High energy physics instrumentation computing; Information analysis; Multiple signal classification; Music; Performance analysis; Power harmonic filters; Signal generators; Source separation; Statistical distributions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Applications of Signal Processing to Audio and Acoustics, 2003 IEEE Workshop on.
  • Print_ISBN
    0-7803-7850-4
  • Type

    conf

  • DOI
    10.1109/ASPAA.2003.1285861
  • Filename
    1285861