Using Source Separation to Improve Tempo Detection

We describe a novel tempo estimation method based on decomposing musical audio into sources using principal latent component analysis (PLCA). The approach is motivated by the observation that in rhythmically complex music, some layers may be more rhythmically regular than the overall mix, thus facilitating tempo detection. Each excerpt was analyzed using PLCA and the resulting components were each tempo tracked using a standard autocorrelationbased algorithm. We describe several techniques for aggregating or choosing among the multiple estimates that result from this process to extract a global tempo estimate. The system was evaluated on the MIREX 2006 training database as well as a newly constructed database of rhythmically complex electronic music consisting of 27 examples (IDM DB). For these databases the algorithms improved accuracy by 10% (60% vs 50%) and 22.3% (48.2% vs. 25.9%) respectively. These preliminary results suggest that for some types of music, source-separation may lead to better tempo detection.

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