SymCHM—an unsupervised approach for pattern discovery in symbolic music with a compositional hierarchical model

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

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.

• Language: English
• Keywords: music information retrieval, compositional modelling, pattern discovery, symbolic music representations
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2017
• Number of pages: 20 str.
• Numbering: Vol. 7, iss. 11, art. 1135
• PID: 20.500.12556/RUL-131009
• UDC: 004:78
• ISSN on article: 2076-3417
• DOI: 10.3390/app7111135
• COBISS.SI-ID: 1537631683
• Publication date in RUL: 21.09.2021

Record is a part of a journal

• Title: Applied sciences
• Shortened title: Appl. sci.
• Publisher: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 04.11.2017

Secondary language

• Language: Slovenian
• Keywords: pridobivanje informacij iz glasbe, kompozicionalno modeliranje, odkrivanje vzorcev, simbolne predstavitve glasbe