# Interpretability of Methods for Switch Point Detection in Electronic Dance Music
## 1 Introduction  
This study concerns the automatic detection of a particular type of cue point that we name switch point. These are positions of interest within a song for the construction of uninterrupted streams of music and are especially relevant in the context of electronic dance music (EDM) and DJ mixing because they are used to juxtapose one song after another. In this work, we compare and contrast different computational approaches, focusing not only on their accuracy, but also on their interpretability. The job of DJs is to produce continuous streams of music out of individual tracks, thereby inducing specific emotional reactions in the audience. While a continuous stream of music can be accomplished rather trivially by concatenating music tracks, DJs control the emotional reactions of the audience through a creative, yet structured process that involves the careful selection of where and how one track transitions into another [1]. The transition is constituted of three elements: A segment during which the old track is dominant, the switch point, which is when the new track takes over the previous one, and a segment during which the new track is dominant. Either of these two segments might be absent, while the switch point is present in all transitions [2]. The significance of transitions for a DJ is twofold: On the one hand, they represent the affordances that a track offers to steer the mood and the energy of an audience toward different directions; on the other hand, they are the gateways to learn how mood and energy are handled by DJs. In this context, switch points are effectively points of articulation in the DJ's discourse and in the way that discourse manipulates the audience's limbic response. They are moments in time when the current track hands over the musical lead to the upcoming track and, consequently, when the attention of the listeners is supposed to shift from the current track to the next one. To find switch points is arguably a form of conducting a structural analysis of a DJ mix, both musically and conceptually [3, 4]. Therefore, the accurate detection of switch points correlates directly to our ability to understand how a DJ mix is constructed. There exist multiple approaches for the task of switch point detection (SPD). In this work, we evaluate and interpret different methods, one experience- and several statistics-driven (linear discriminant analysis, decision tree, and gradient boosting decision trees). The former establishes a priori the features that are known to correlate with switch points and returns positions where novelty in those features is maximized. By contrast, the latter build on a carefully curated dataset which is used to construct (\"learn\") an a priori unknown association between musical features and the already identified switch points in the dataset; such an association is then applied to any arbitrary song to return switch point candidates. In all cases, the applicability of a method is constrained by its confidence level, which in turn is related to both the accuracy achieved when testing on a curated dataset, and the size and quality of the dataset itself. This study expands on a previous one which was limited to only two methods [2]. These approaches and methods contribute differently to our understanding of DJ mixes. On the one hand, statistical methods have the potential to uncover complex and unknown relationships among features (under the assumption that the models learned are interpretable). On the other hand, the experience-driven method can validate or disprove human intuition. In the first case, we learn from the method that, statistically, humans tend to identify a position as a switch point when certain combinations of features occur. In the second case, such combinations derive from human experience and are encoded as rules directly into the algorithm.