Enhancing downbeat detection when facing different music styles

Simon Durand, Bertrand David, Gael Richard
<span title="">2014</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rc5jnc4ldvhs3dswicq5wk3vsq" style="color: black;">2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</a> </i> &nbsp;
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
more &raquo; ... aluated on five different datasets of various musical styles and shows improvement over the state of the art.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/icassp.2014.6854177">doi:10.1109/icassp.2014.6854177</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/icassp/DurandDR14.html">dblp:conf/icassp/DurandDR14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tym5inplcbbcffzn5kj4wwgbia">fatcat:tym5inplcbbcffzn5kj4wwgbia</a> </span>
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