Abstract
Switch points are a specific kind of cue point that DJs carefully look for when mixing music tracks. As the name says, a switch point is the point in time where the current track in a DJ mix is replaced by the upcoming track. Being able to identify these positions is a first step toward the interpretation and the emulation of DJ mixes. With the aim of automatically detecting switch points, we evaluate one experience-driven and several statistics-driven methods. By comparing the decision process of each method, contrasted by their performance, we deduce the characteristics linked to switch points. Specifically, we identify the most impactful features for their detection, namely, the novelty in the signal energy, the timbre, the number of drum onsets, and the harmony. Furthermore, we expose multiple interactions among these features.
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