An analysis of rhythmic patterns with unsupervised learning

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

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.

• Jezik: Angleški jezik
• Ključne besede: music information retrieval, rhythm analysis, compositional hieararchical model
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2020
• Št. strani: 22 str.
• Številčenje: Vol. 10, iss. 1, art. 178
• PID: 20.500.12556/RUL-133130
• UDK: 004:78
• ISSN pri članku: 2076-3417
• DOI: 10.3390/app10010178
• COBISS.SI-ID: 1538490051
• Datum objave v RUL: 12.11.2021

PESEK, Matevž, LEONARDIS, Aleš in MAROLT, Matija, 2020, An analysis of rhythmic patterns with unsupervised learning. Applied sciences [na spletu]. 2020. Vol. 10, no. 1,  178. [Dostopano 9 april 2025]. DOI 10.3390/app10010178. Pridobljeno s: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=slv&id=133130

Gradivo je del revije

• Naslov: Applied sciences
• Skrajšan naslov: Appl. sci.
• Založnik: MDPI
• ISSN: 2076-3417
• COBISS.SI-ID: 522979353

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 01.01.2020

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: pridobivanje informacij iz glasbe, analiza ritma, kompozicionalni hierarhični model