Music rhythm characterization with application to workout-mix generation

In this paper, we present approaches to musical rhythm pattern extraction, rhythm-based music retrieval, and rhythm-synchronized music mixing. A probabilistic model is used to jointly estimate tempo and time signature as a basis for beat tracking and measure detection. A representative rhythm pattern is then extracted through clustering to characterize the rhythm of a song. Based on this, a probabilistic approach is used for retrieving songs with similar rhythmic patterns. These are then mixed rhythm-synchronously with transitions maintaining continuity and regularity of beats. We apply the presented methods into workout-mix generation, which aims at automatically selecting rhythmically similar music given a seed song and a user-defined tempo profile. Our probabilistic approaches achieve accuracies similar to best published results, but avoid manually tuned parameters and "fudge factors".