# Interpretability of Methods for Switch Point Detection in Electronic Dance Music
## 3 Methods
### Experience-Driven  
From the analysis of hundreds of DJ mixes, we concluded that two aspects of a track are especially important for SPD: A change in the energy of the signal--as related to the introduction of the bass register (e.g., with a bass guitar or a change in filtering)--and the entrance of the bass drum. Since these elements are two of the driving forces of EDM and are unquestionably linked to the danceability of a track, the features we are interested in extracting concern mostly their presence. This choice is not only a departure from the methods presented in the related work section, which rely on features associated to timbre and harmony, but is also advantageous for considering features that can be used for the detection of the core section of the track--indeed, we expect that one of the switch points lies exactly at the beginning of the core. However, one notable difference between the two tasks is that for SPD, we rely on the novelty and not on the amplitude of the features, which is instead used for the detection of the core section.  
By considering novelty, we can handle two edge cases that would not otherwise be correctly interpreted when considering the amplitude of the features alone. (1) The case of a core section gradually faded in will display high amplitude in the features but not high novelty. As such, it will not lead to the identification of a switch point. This is a desirable outcome as we expect switch points to be positions of salient novelty. (2) The case of a breakdown before the beginning of the core, characterized by a sudden decrease in the amplitude of features and high novelty, will likely be selected as a switch point. This happens because novelty does not depend on the direction of the change, e.g., the progression in the features may be either from high amplitude to low amplitude, or the other way around. This is also a desirable outcome as the start of a breakdown is a valid switch position before the beginning of the core section of the track.  
In the following, the detection of the bass register and the bass drum is obtained via the novelty of two different features. Novelty in the bass register is associated with a change in signal energy, which in turn is obtained by calculating the Root Mean Square (RMS) of the signal with Librosa [27]. This quantity is closely related to the perceived loudness of the signal, even though it does not account for the perception of the human ear, which is less sensitive to audio frequencies in the low range. As a result, the RMS of the signal puts a strong emphasis on the bass frequencies. Novelty in the bass drum is associated with the number of onsets in a specific span of time, and its computation relies on automatic drum transcription (ADT) algorithms, as in [28]. The features (i.e., signal energy and onsets numbers) are computed in non-overlapping two-beat windows (between the 1 st and the 3 rd beat of a 4/4 bar). We choose this window size because we noticed that in many drum patterns, the bass drum emphasizes only the 1 st and 3 rd beats of a bar (as happens in the standard rock pattern). As observed in other approaches discussed in the related work section, this coarse window size effectively ignores the fine-grained variations in features between beats. A coarser window of one bar (between downbeats) has also been evaluated, but we noticed that the downbeats were not accurately identified in EDM with the software we used from [29]. For each feature, one SSM is built, and CK is applied to compute a novelty with a 32-beat long kernel to compare four-bar periods. Finally, the decision step selects the maximal position in each of the two novelty curves within the search space identified earlier.