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Automatic Modeling of Musical Style

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text
 

Genre(s)

article
 

Forme(s)

document numérique
 

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Titre

Automatic Modeling of Musical Style
 

Nom(s)

Lartillot, Olivier (auteur)
 
Dubnov, Shlomo (auteur)
 
Assayag, Gérard (auteur)
 
Bejerano, Gill (auteur)
 

Publication

La Havane, Cuba , 2001
 

Description

Sujet(s)

Unsupervised learning   Musical style   Compression   Predition Suffix Tree (PST)   Probabilistic Finite Automata (PSA)   Lempel-Ziv (LZ)   Stochastic   Quantization   Constraints   Loop   Redundancy   Musical Parameters   Markov predictor.
 

Résumé

In this paper, we describe and compare two methods for unsupervised learning of musical style, both of which perform analyses of musical sequences and then compute a model from which new interpretations / improvisations close to the original's style can be generated. In both cases, an important part of the musical structure is captured, including rhythm, melodic contour, and polyphonic relationships. The first method is a drastic improvement of the Incremental Parsing (IP) method, a method derived from compression theory and proven useful in the musical domain. The second one is an application to music of Prediction Suffix Trees (PST), a learning technique initially developed for statistical modeling of complex sequences with applications in linguistics and biology.
 

Note(s)

Contribution au colloque ou congrès : International Computer Music Conference
 

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Date de la notice

2010-02-25 01:00:00
 

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