ǂAn ǂanalysis of rhythmic patterns with unsupervised learning

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

Abstract

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.

Keywords

pridobivanje informacij iz glasbe;analiza ritma;kompozicionalni hierarhični model;music information retrieval;rhythm analysis;compositional hiearchical model;

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UL FRI - Faculty of Computer and Information Science
UDC:	004:78
COBISS:	1538490051
ISSN:	2076-3417
Views:	174
Downloads:	61
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	Slovenian
Secondary keywords:	pridobivanje informacij iz glasbe;analiza ritma;kompozicionalni hierarhični model;
Type (COBISS):	Article
Pages:	str. 1-22
Volume:	ǂVol. ǂ10
Issue:	ǂiss. ǂ1
Chronology:	Jan. 2020
DOI:	10.3390/app10010178
ID:	13925506