## 1 Introduction
The MTP with a defined particular vector is a set of points, which can be translated by that vector to give other points in the point-set representation. The authors observed that the MTPs often correspond to perceptually significant repeated patterns in music. The TEC defines a set of all patterns which are translationally equivalent to a pattern defining the specific TEC. The SIATECCompressSegment approach generates an ordered list of TECs which may overlap (in contrast to other related versions such as COSIATEC). Recently, [PERSON] and [PERSON] [32] extended a previously introduced approach to melodic segmentation [33] for melodic classification and segmentation, where the symbolic input is first segmented, then compared and hierarchically clustered. Finally, the clusters are ranked, taking into account the cumulative length of all occurrences within each cluster. Based on their results, it can be assumed that the output is additionally filtered by a threshold defining the number of output patterns. [PERSON] [34] introduced the PatMirn algorithm [35] which uses an incremental one-pass approach to identify pattern occurrences. To avoid redundancy, the author addresses two issues: closed pattern mining, which filters out the patterns that have more occurrences than their more specific patterns, thus providing more robust patterns, and pattern cyclicity, which removes redundant matches for successive occurrences of a single underlying pattern. The most recent approach submitted to the MIREX task by [PERSON] [36] also employs a closed pattern approach commonly used in data mining. [PERSON] and [PERSON] [37] proposed the MotivesExtractor which obtains a harmonic representation of the audio or symbolic input and extracts patterns based on a produced self-similarity matrix. Using a score-based greedy algorithm ([38]) the approach extracts repeated segments, allowing the patterns to overlap. Finally, the segments are grouped into clusters and provided in the algorithm's output as patterns. In contrast to the existing hierarchical and deep approaches, the Compositional Hierarchical Model (CHM) presented in this paper is a transparent deep architecture. The model provides an explicit (transparent) encoding of concepts, learned in an unsupervised manner, thus merging the benefits of explicit and deep hierarchical models in MIR. The CHM is built around the premise that the repetitive nature of patterns can be captured by observing statistics of occurrences of their sub-patterns, thus providing a hierarchy of the analysed symbolic music representation(s) [39]. Similar to other approaches that build a tree of patterns based on their subsumption (e.g., [25]), the CHM first extracts small atomic patterns and builds complex patterns as compositions of these atomic patterns. Its ability to concurrently provide multiple pattern hypotheses on several levels of complexity and their transparent descriptions makes it very suitable for pattern extraction, as patterns may overlap or be mutually included. The compositional hierarchical model was first introduced by [PERSON] et al. [40] and was evaluated for several MIR tasks, including automated chord estimation and multiple fundamental frequency estimation [41]. In the paper, we present an adaptation of the model for analysis of Symbolic music (SymCHM) applied to the task of finding repeated patterns and sections. Instead of finding compositions in a frequency-magnitude audio representation, the adjusted model searches for compositions of symbolic events in the time-pitch-onset domain. The model learns a hierarchy of patterns; the transparent nature of the model allows the user to explore and analyse a music piece by observing the hierarchy of pattern occurrences. For the automatic discovery of repeated patterns, the patterns represented in the hierarchy are extracted. We analyse the model output and propose an extension of the model named SymCHMMerge, which refines the extracted patterns.