SymCHM—an unsupervised approach for pattern discovery in symbolic music with a compositional hierarchical modelPesek, Matevž (Avtor) Leonardis, Aleš (Avtor) Marolt, Matija (Avtor) music information retrievalcompositional modellingpattern discoverysymbolic music representationsThis paper presents a compositional hierarchical model for pattern discovery in symbolic music. The model can be regarded as a deep architecture with a transparent structure. It can learn a set of repeated patterns within individual works or larger corpora in an unsupervised manner, relying on statistics of pattern occurrences, and robustly infer the learned patterns in new, unknown works. A learned model contains representations of patterns on different layers, from the simple short structures on lower layers to the longer and more complex music structures on higher layers. A pattern selection procedure can be used to extract the most frequent patterns from the model. We evaluate the model on the publicly available JKU Patterns Datasets and compare the results to other approaches.20172021-09-21 12:15:24Članek v reviji131009UDK: 004:78ISSN pri članku: 2076-3417DOI: 10.3390/app7111135COBISS_ID: 1537631683sl