ǂAn ǂanalysis of rhythmic patterns with unsupervised learning

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

Povzetek

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.

Ključne besede

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

Podatki

Jezik:	Angleški jezik
Leto izida:	2020
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UL FRI - Fakulteta za računalništvo in informatiko
UDK:	004:78
COBISS:	1538490051
ISSN:	2076-3417
Št. ogledov:	174
Št. prenosov:	61
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

Sekundarni jezik:	Slovenski jezik
Sekundarne ključne besede:	pridobivanje informacij iz glasbe;analiza ritma;kompozicionalni hierarhični model;
Vrsta dela (COBISS):	Članek v reviji
Strani:	str. 1-22
Letnik:	ǂVol. ǂ10
Zvezek:	ǂiss. ǂ1
Čas izdaje:	Jan. 2020
DOI:	10.3390/app10010178
ID:	13925506