Computational Fugue Analysis

Author:

Giraud Mathieu1,Groult Richard2,Leguy Emmanuel1,Levé Florence2

Affiliation:

1. Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) UMR 9189 (CNRS, Université de Lille) Cité Scientifique 59 655 Villeneuve d'Ascq, France

2. Laboratoire Modélisation, Information et Systèmes (MIS) Université Picardie Jules Verne 33 rue Saint Leu 80 039 Amiens cedex 1, France

Abstract

One of the pinnacles of form in classical Western music, the fugue is often used in the teaching of music analysis and composition. Fugues alternate between instances of a subject and other patterns and modulatory sections, called episodes. Musicological analyses are generally built on these patterns and sections. We have developed several algorithms to perform an automated analysis of a fugue, starting from a score in which all the voices are separated. By focusing on the diatonic similarities between pitch intervals, we detect subjects and countersubjects, as well as partial harmonic sequences inside the episodes. We also implemented tools to detect subject scale degrees, cadences, and pedals, as well as a method for segmenting the fugue into exposition and episodic parts. Our algorithms were tested on a corpus of 36 fugues by J. S. Bach and Dmitri Shostakovich. We provide formalized ground-truth data on this corpus as well as a dynamic visualization of the ground truth and of our computed results. The complete system showed acceptable or good results for about one half of the fugues tested, enabling us to depict their design.

Publisher

MIT Press - Journals

Subject

Computer Science Applications,Music,Media Technology

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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3. Identification and Classification of Music Genre using Deep Learning;2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA);2022-09-08

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