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
Link To Document :
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