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A probabilistic framework for audio-based tonal key and chord recognition

Benoit Catteau UGent, Jean-Pierre Martens UGent and Marc Leman UGent (2007) STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION. p.637-644
abstract
A unified probabilistic framework for audio-based chord and tonal key recognition is described and evaluated. The proposed framework embodies an acoustic observation likelihood model and key & chord transition models. It is shown how to conceive these models and how to use music theory to link key/chord transition probabilities to perceptual similarities between keys/chords. The advantage of a theory based model is that it does not require any training, and consequently, that its performance is not affected by the quality of the available training data.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
ALGORITHM
in
STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION
editor
R Decker and HJ Lenz
issue title
Advances in Data Analysis
pages
637 - 644
publisher
Springer
place of publication
Berlin, Germany
conference name
30th Annual Conference of the German-Classification-Society
conference location
Berlin, Germany
conference start
2006-03-08
conference end
2006-03-10
Web of Science type
Proceedings Paper
Web of Science id
000246055000073
ISSN
1431-8814
ISBN
9783540709800
language
English
UGent publication?
yes
classification
P1
id
386772
handle
http://hdl.handle.net/1854/LU-386772
date created
2008-02-10 18:06:00
date last changed
2017-01-02 09:52:37
@inproceedings{386772,
  abstract     = {A unified probabilistic framework for audio-based chord and tonal key recognition is described and evaluated. The proposed framework embodies an acoustic observation likelihood model and key \& chord transition models. It is shown how to conceive these models and how to use music theory to link key/chord transition probabilities to perceptual similarities between keys/chords. The advantage of a theory based model is that it does not require any training, and consequently, that its performance is not affected by the quality of the available training data.},
  author       = {Catteau, Benoit and Martens, Jean-Pierre and Leman, Marc},
  booktitle    = {STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION},
  editor       = {Decker, R and Lenz, HJ},
  isbn         = {9783540709800},
  issn         = {1431-8814},
  keyword      = {ALGORITHM},
  language     = {eng},
  location     = {Berlin, Germany},
  pages        = {637--644},
  publisher    = {Springer},
  title        = {A probabilistic framework for audio-based tonal key and chord recognition},
  year         = {2007},
}

Chicago
Catteau, Benoit, Jean-Pierre Martens, and Marc Leman. 2007. “A Probabilistic Framework for Audio-based Tonal Key and Chord Recognition.” In Studies in Classification, Data Analysis, and Knowledge Organization, ed. R Decker and HJ Lenz, 637–644. Berlin, Germany: Springer.
APA
Catteau, B., Martens, J.-P., & Leman, M. (2007). A probabilistic framework for audio-based tonal key and chord recognition. In R. Decker & H. Lenz (Eds.), STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION (pp. 637–644). Presented at the 30th Annual Conference of the German-Classification-Society, Berlin, Germany: Springer.
Vancouver
1.
Catteau B, Martens J-P, Leman M. A probabilistic framework for audio-based tonal key and chord recognition. In: Decker R, Lenz H, editors. STUDIES IN CLASSIFICATION, DATA ANALYSIS, AND KNOWLEDGE ORGANIZATION. Berlin, Germany: Springer; 2007. p. 637–44.
MLA
Catteau, Benoit, Jean-Pierre Martens, and Marc Leman. “A Probabilistic Framework for Audio-based Tonal Key and Chord Recognition.” Studies in Classification, Data Analysis, and Knowledge Organization. Ed. R Decker & HJ Lenz. Berlin, Germany: Springer, 2007. 637–644. Print.