Enhancing downbeat detection when facing different music styles

Simon Durand 1, 2 Bertrand David 1, 2 Gael Richard 1, 2
1 S2A - Signal, Statistique et Apprentissage
LTCI - Laboratoire Traitement et Communication de l'Information
Abstract :

This paper focuses on the automatic rhythm analysis of musical audio at the bar level. We propose a novel approach for robust downbeat detection. It uses well-chosen complementary features, inspired by musical considerations. In particular, a note accentuation model and a detection of pattern changes are introduced. We estimate the time signature by examining the similarity of frames at the beat level. The features are selected through a linear SVM model or a weighted sum. The whole system is evaluated on five different datasets of various musical styles and shows improvement over the state of the art.

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Submitted on : Friday, September 13, 2019 - 4:20:55 PM
Last modification on : Thursday, October 17, 2019 - 12:37:02 PM


  • HAL Id : hal-02286904, version 1



Simon Durand, Bertrand David, Gael Richard. Enhancing downbeat detection when facing different music styles. ICASSP, May 2014, Florence, Italy. pp.3152-3156. ⟨hal-02286904⟩



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