An analysis of rhythmic patterns with unsupervised learning

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

This paper presents a model capable of learning the rhythmic characteristics of a music signal through unsupervised learning. The model learns a multi-layer hierarchy of rhythmic patterns ranging from simple structures on lower layers to more complex patterns on higher layers. The learned hierarchy is fully transparent, which enables observation and explanation of the structure of the learned patterns. The model employs tempo-invariant encoding of patterns and can thus learn and perform inference on tempo-varying and noisy input data. We demonstrate the model’s capabilities of learning distinctive rhythmic structures of different music genres using unsupervised learning. To test its robustness, we show how the model can efficiently extract rhythmic structures in songs with changing time signatures and live recordings. Additionally, the model’s time-complexity is empirically tested to show its usability for analysis-related applications.

• Language: English
• Keywords: music information retrieval, rhythm analysis, compositional hieararchical model
• 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: 2020
• Number of pages: 22 str.
• Numbering: Vol. 10, iss. 1, art. 178
• PID: 20.500.12556/RUL-133130
• UDC: 004:78
• ISSN on article: 2076-3417
• DOI: 10.3390/app10010178
• COBISS.SI-ID: 1538490051
• Publication date in RUL: 12.11.2021

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: 01.01.2020

Secondary language

• Language: Slovenian
• Keywords: pridobivanje informacij iz glasbe, analiza ritma, kompozicionalni hierarhični model