# Interpretability of Methods for Switch Point Detection in Electronic Dance Music
## 2 Related Work
### Methods for Switch Point Detection  
Besides the choice of a computational method to detect segment boundaries, care has to be devoted to the selection of the features that better characterize a boundary. In fact, not all features are of equal importance, making them suitable or not to account for segmentation principles [14]. As [PERSON] and [PERSON] [13] state, depending on the music genre, timbre can be homogeneous within a segment but might not display a highly repetitive behavior (e.g., the instrumentation can be homogeneous within a section but may change between repetitions of the same section). On the contrary, chroma-like features might not remain homogeneous in a segment, but they may show repetitive patterns (e.g., the chord progression in a section will be likely repeated multiple times throughout the track). The combination of features (e.g., timbre or harmony) and principles (e.g., homogeneity or repetition) used in a method determines which patterns can be caught. As we will see below, many of the methods for SPD discussed in the literature are based on observations and intuition related to musical patterns. [PERSON] et al. 's [15] is one of the earliest works to explicitly rely on MSA for the segmentation of EDM tracks. [PERSON]'s approach focuses on local novelty in timbre, motivated by what [PERSON] [1] states in Unlocking the Groove, a seminal work on EDM's musical behavior: Timbral novelty in EDM involves a change in instrumentation at a structural boundary. Therefore, [PERSON] uses CK with the mel-frequency cepstral coefficients (MFCCs) feature to represent local novelty in timbre. The boundaries are then selected by PP and, finally, the peaks are quantized to the closest downbeat of a period, thus exploiting also the regularity principle of EDM. This method is evaluated on an in-house EDM dataset with good results. In a similar fashion, [PERSON] and [PERSON] [16] perform CK with MFCC to represent local changes in timbre, and improve the results by computing local changes in the signal energy, another aspect of EDM mentioned in [PERSON]'s work. This is performed by averaging the novelty curves for both features (timbre and energy), selecting the boundaries with PP, and then, in accordance with what the regularity principle asserts, filtering them with a period quantization. This method was evaluated positively but was never extended to other musical genres besides the specific one targeted by the authors, that is, Drum and Bass. The novelty and regularity principles provide a foundation also for [PERSON] et al. 's approach [17]. The authors employ CK on a semitone-spectrogram representing harmony to detect period boundaries. However, when observed at a fine granularity (e.g., when comparing every beat), harmony within a section is usually not as homogeneous as timbre. Thus, [PERSON] method agglomerates the semitone-spectrogram vectors to a coarser downbeat granularity that effectively computes homogeneity of the harmonic sequence at bar level. This is done with a so-called time-lag embedding (different from the time-lag representation computed by SF). This method has been proved to reliably identify structural segmentation boundaries with precise temporal localizations. [PERSON] and al. [18] rely on the principle of repetition of harmonic patterns. They aim at identifying drop locations from segment boundaries returned by SF built from Pitch Class Profiles (PCPs). The authors' experiments indicate that this method has a high recall; that is, it captures most of the drop locations. However, due to the frame-synchronous computation of the PCP feature, the distance between the drop location and the position detected is too large (2.5 s on average) to be used in SPD. In fact, this lack of time precision with SF is known and has been improved by [PERSON] and [PERSON] [13] by employing beat-synchronous and median-filtered features. More recently, [PERSON] et al.