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Communication Dans Un Congrès Année : 2014

Enhancing downbeat detection when facing different music styles

Résumé

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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Dates et versions

hal-02286904 , version 1 (13-09-2019)

Identifiants

  • HAL Id : hal-02286904 , version 1

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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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