Matevž Pesek (Author), Aleš Leonardis (Author), Matija Marolt (Author)

Abstract

This 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.

Keywords

pridobivanje informacij iz glasbe;kompozicionalno modeliranje;odkrivanje vzorcev;simbolne predstavitve glasbe;music information retrieval;compositional modelling;pattern discovery;symbolic music representations;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UL FRI - Faculty of Computer and Information Science
UDC: 004:78
COBISS: 1537631683 Link will open in a new window
ISSN: 2076-3417
Views: 176
Downloads: 51
Average score: 0 (0 votes)
Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: Slovenian
Secondary keywords: pridobivanje informacij iz glasbe;kompozicionalno modeliranje;odkrivanje vzorcev;simbolne predstavitve glasbe;
Type (COBISS): Article
Pages: str. 1-21
Volume: ǂVol. ǂ7
Issue: ǂiss. ǂ11
Chronology: Nov. 2017
DOI: 10.3390/app7111135
ID: 13505853