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An Analysis of Rhythmic Patterns with Unsupervised Learning

Faculty of Computer and Information Science, University of Ljubljana, Vecna pot 113, 1000 Ljubljana, Slovenia
School of Computer Science, University of Birmingham, University Rd W, Birmingham B15 2TT, UK
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(1), 178;
Received: 18 October 2019 / Revised: 13 December 2019 / Accepted: 20 December 2019 / Published: 25 December 2019
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. View Full-Text
Keywords: music information retrieval; rhythm analysis; compositional hierarchical model music information retrieval; rhythm analysis; compositional hierarchical model
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MDPI and ACS Style

Pesek, M.; Leonardis, A.; Marolt, M. An Analysis of Rhythmic Patterns with Unsupervised Learning. Appl. Sci. 2020, 10, 178.

AMA Style

Pesek M, Leonardis A, Marolt M. An Analysis of Rhythmic Patterns with Unsupervised Learning. Applied Sciences. 2020; 10(1):178.

Chicago/Turabian Style

Pesek, Matevž, Aleš Leonardis, and Matija Marolt. 2020. "An Analysis of Rhythmic Patterns with Unsupervised Learning" Applied Sciences 10, no. 1: 178.

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