Belief propagation algorithm for automatic chord estimation

Vincent P. Martin, Sylvain Reynal, Dogac Basaran, Hélène Camille Crayencour

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


This work aims at bridging the gap between two completely distinct research fields: digital communications and Music Information Retrieval. While works in the MIR community have long used algorithms borrowed from speech signal processing, text recognition or image processing, to our knowledge very scarce work based on digital communications algorithms has been produced. This paper specifically targets the use of the Belief Propagation algorithm for the task of Automatic Chord Estimation. This algorithm is of widespread use in iterative decoders for error correcting codes and we show that it offers improved performances in ACE by genuinely incorporating the ability to take constraints between distant parts of the song into account. It certainly represents a promising alternative to traditional MIR graphical models approaches, in particular Hidden Markov Models.

Original languageEnglish
Title of host publicationProceedings of the 16th Sound and Music Computing Conference, SMC 2019
EditorsIsabel Barbancho, Lorenzo J. Tardon, Alberto Peinado, Ana M. Barbancho
Number of pages8
ISBN (Electronic)9788409085187
Publication statusPublished - 20 May 2019
Externally publishedYes
Event16th Sound and Music Computing Conference, SMC 2019 - Malaga, Spain
Duration: 28 May 201931 May 2019

Publication series

NameProceedings of the Sound and Music Computing Conferences
ISSN (Print)2518-3672


Conference16th Sound and Music Computing Conference, SMC 2019


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