Audio chord labeling by musiological modeling and beat-synchronization

Björn Schuller, Benedikt Hörnler, Dejan Arsic, Gerhard Rigoll

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

3 Scopus citations

Abstract

Automatic labeling of chords in original audio recordings is challenging due to heavy acoustic overlay by melody and percussion sections, detuning and arpeggios that demand for a measure-grid to assign notes to chords. Further chord labeling benefits from contextual information. In this respect we suggest applying an HMM framework incorporating a musiological model trained on 16k songs and synchronization with the measure grid by IIR comb-filter banks for tempo detection, meter recognition, and on-beat tracking. Features base on pitch-tuned chromatic information. Extensive evaluation on 11k chords of 7h of MP3 compressed popular music demonstrates effectiveness over traditional correlation analysis and single measure classification by Support Vector Machines.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Pages526-529
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Multimedia and Expo, ICME 2009 - New York, NY, United States
Duration: 28 Jun 20093 Jul 2009

Publication series

NameProceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009

Conference

Conference2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Country/TerritoryUnited States
CityNew York, NY
Period28/06/093/07/09

Keywords

  • Feature extraction
  • Hidden Markov models
  • Music

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