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