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A probabilistic approach to simultaneous extraction of beats and downbeats

  • Maksim Khadkevich
  • , Thomas Fillon
  • , Gael Richard
  • , Maurizio Omologo
  • FBK-irst
  • CNRS LTCI

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Résumé

This paper focuses on the automatic extraction of beat structure from a musical piece. A novel statistical approach to modeling beat sequences based on the application of Hidden Markov Models (HMM) is introduced. The resulting beat labels are obtained by running the Viterbi decoder and subsequent lattice rescoring. For the observation vectors we propose a new feature set that is based on the impulsive and harmonic components of the reassigned spectrogram. Different components of observation vectors have been investigated for their efficiency. The main advantage of the proposed approach is the absence of imposed deterministic rules. All the parameters are learned from the training data, and the experimental results show the efficiency of the proposed schema.

langue originaleAnglais
titre2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages445-448
Nombre de pages4
Les DOIs
étatPublié - 23 oct. 2012
Modification externeOui
Evénement2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japon
Durée: 25 mars 201230 mars 2012

Série de publications

NomICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (imprimé)1520-6149

Une conférence

Une conférence2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Pays/TerritoireJapon
La villeKyoto
période25/03/1230/03/12

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