Measuring the Structural Complexity of Music: From Structural Segmentations to the Automatic Evaluation of Models for Music Generation

de Berardinis, Jacopo, Cangelosi, Angelo and Coutinho, Eduardo ORCID: 0000-0001-5234-1497 (2022) Measuring the Structural Complexity of Music: From Structural Segmentations to the Automatic Evaluation of Models for Music Generation. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 30. pp. 1963-1976.

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Official URL: http://dx.doi.org/10.1109/taslp.2022.3178203

Abstract

Composing musical ideas longer than motifs or figures is still rare in music generated by machine learning methods, a problem that is commonly referred to as the lack of long-term structure in the generated sequences. In addition, the evaluation of the structural complexity of artificial compositions is still a manual task, requiring expert knowledge, time and involving subjectivity which is inherent in the perception of musical structure. Based on recent advancements in music structure analysis, we automate the evaluation process by introducing a collection of metrics that can objectively describe structural properties of the music signal. This is done by segmenting music hierarchically, and computing our metrics on the resulting hierarchies to characterise the decomposition process of music into its structural components. We tested our method on a dataset collecting music with different degrees of structural complexity, from random and computer-generated pieces to real compositions of different genres and formats. Results indicate that our method can discriminate between these classes of complexity and identify further non-trivial subdivisions according to their structural properties. Our work contributes a simple yet effective framework for the evaluation of music generation models in regard to their ability to create structurally meaningful compositions.

Item Type:	Article
Uncontrolled Keywords:	Music, Complexity theory, Measurement, Speech processing, Computational modeling, Multiple signal classification, Machine learning, Music structure analysis, evaluation measures
Divisions:	Faculty of Humanities and Social Sciences > School of the Arts
Depositing User:	Symplectic Admin
Date Deposited:	04 May 2022 11:07
Last Modified:	18 Jan 2023 21:04
DOI:	10.1109/TASLP.2022.3178203
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3154275