A probabilistic approach to simultaneous extraction of beats and downbeats

Maksim Khadkevich, Thomas Fillon, Gael Richard, Maurizio Omologo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages445-448
Number of pages4
DOIs
Publication statusPublished - 23 Oct 2012
Externally publishedYes
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

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

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

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