Learning Sonata Form Structure on Mozart's ...
Document type :
Compte-rendu et recension critique d'ouvrage
DOI :
Title :
Learning Sonata Form Structure on Mozart's String Quartets
Author(s) :
Allegraud, Pierre [Auteur]
Algomus
Bigo, Louis [Auteur]
Algomus
Feisthauer, Laurent [Auteur]
Algomus
Giraud, Mathieu [Auteur]
Algomus
Groult, Richard [Auteur]
Algomus
Leguy, Emmanuel [Auteur]
Algomus
Leve, Florence [Auteur]
Algomus
Modélisation, Information et Systèmes - UR UPJV 4290 [MIS]
Algomus
Bigo, Louis [Auteur]

Algomus
Feisthauer, Laurent [Auteur]
Algomus
Giraud, Mathieu [Auteur]

Algomus
Groult, Richard [Auteur]

Algomus
Leguy, Emmanuel [Auteur]
Algomus
Leve, Florence [Auteur]

Algomus
Modélisation, Information et Systèmes - UR UPJV 4290 [MIS]
Journal title :
Transactions of the International Society for Music Information Retrieval (TISMIR)
Pages :
82-96
Publisher :
Ubiquity Press
Publication date :
2019
ISSN :
2514-3298
English keyword(s) :
Computational Music Analysis
Music Structure
Musical Form
Sonata Form
Music Structure
Musical Form
Sonata Form
HAL domain(s) :
Sciences de l'Homme et Société/Musique, musicologie et arts de la scène
Informatique [cs]/Son [cs.SD]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Son [cs.SD]
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [en]
The musical analysis of large-scale structures, such as the classical sonata form, requires to integrate multiple analyses of local musical events into a global coherent analysis. Modelling large-scale structures is still ...
Show more >The musical analysis of large-scale structures, such as the classical sonata form, requires to integrate multiple analyses of local musical events into a global coherent analysis. Modelling large-scale structures is still a challenging task for the research community. It includes building large and accurate annotated corpora, as well as developing practical and efficient tools in order to visualize the analyses of these corpora. It finally requires the conception of effective and properly evaluated MIR algorithms.We propose a machine learning approach for the sonata form structure on 32 movements from Mozart’s string quartets. We release an open dataset, encoding two reference analyses of these 32 movements, totaling more than 1800 curated annotations, as well as flexible visualizations of these analyses. We discuss the occurrence in this corpus of melodic, harmonic, and rhythmic features induced by pitches, durations, and rests. We investigate whether the presence or the absence of these features can be characteristic of the different sections forming a sonata form. We then compute the emission and transition probabilities of several Hidden Markov Models intended to match the structure of sonata forms at several resolutions. Our results confirm that the sonata form is better identified when the parameters are learned rather than manually set up. These results open perspectives on the computational analysis of musical forms by mixing human knowledge and machine learning from annotated scores.Show less >
Show more >The musical analysis of large-scale structures, such as the classical sonata form, requires to integrate multiple analyses of local musical events into a global coherent analysis. Modelling large-scale structures is still a challenging task for the research community. It includes building large and accurate annotated corpora, as well as developing practical and efficient tools in order to visualize the analyses of these corpora. It finally requires the conception of effective and properly evaluated MIR algorithms.We propose a machine learning approach for the sonata form structure on 32 movements from Mozart’s string quartets. We release an open dataset, encoding two reference analyses of these 32 movements, totaling more than 1800 curated annotations, as well as flexible visualizations of these analyses. We discuss the occurrence in this corpus of melodic, harmonic, and rhythmic features induced by pitches, durations, and rests. We investigate whether the presence or the absence of these features can be characteristic of the different sections forming a sonata form. We then compute the emission and transition probabilities of several Hidden Markov Models intended to match the structure of sonata forms at several resolutions. Our results confirm that the sonata form is better identified when the parameters are learned rather than manually set up. These results open perspectives on the computational analysis of musical forms by mixing human knowledge and machine learning from annotated scores.Show less >
Language :
Anglais
Popular science :
Non
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