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Open AccessArticle

SymCHM—An Unsupervised Approach for Pattern Discovery in Symbolic Music with a Compositional Hierarchical Model

1
Faculty of Computer and Information Science, University of Ljubljana, Ljubljana 1000, Slovenia
2
School of Computer Science, University of Birmingham, Edgbaston, Birmingham B15 2TT, U.K
*
Author to whom correspondence should be addressed.
Academic Editor: Meinard Müller
Appl. Sci. 2017, 7(11), 1135; https://doi.org/10.3390/app7111135
Received: 13 September 2017 / Revised: 25 October 2017 / Accepted: 1 November 2017 / Published: 4 November 2017
(This article belongs to the Special Issue Sound and Music Computing)
This paper presents a compositional hierarchical model for pattern discovery in symbolic music. The model can be regarded as a deep architecture with a transparent structure. It can learn a set of repeated patterns within individual works or larger corpora in an unsupervised manner, relying on statistics of pattern occurrences, and robustly infer the learned patterns in new, unknown works. A learned model contains representations of patterns on different layers, from the simple short structures on lower layers to the longer and more complex music structures on higher layers. A pattern selection procedure can be used to extract the most frequent patterns from the model. We evaluate the model on the publicly available JKU Patterns Datasetsand compare the results to other approaches. View Full-Text
Keywords: music information retrieval; compositional modelling; pattern discovery; symbolic music representations music information retrieval; compositional modelling; pattern discovery; symbolic music representations
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MDPI and ACS Style

Pesek, M.; Leonardis, A.; Marolt, M. SymCHM—An Unsupervised Approach for Pattern Discovery in Symbolic Music with a Compositional Hierarchical Model. Appl. Sci. 2017, 7, 1135. https://doi.org/10.3390/app7111135

AMA Style

Pesek M, Leonardis A, Marolt M. SymCHM—An Unsupervised Approach for Pattern Discovery in Symbolic Music with a Compositional Hierarchical Model. Applied Sciences. 2017; 7(11):1135. https://doi.org/10.3390/app7111135

Chicago/Turabian Style

Pesek, Matevž; Leonardis, Aleš; Marolt, Matija. 2017. "SymCHM—An Unsupervised Approach for Pattern Discovery in Symbolic Music with a Compositional Hierarchical Model" Appl. Sci. 7, no. 11: 1135. https://doi.org/10.3390/app7111135

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