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Journal Articles Computer Music Journal Year : 2019

Learning of Hierarchical Temporal Structures for Guided Improvisation

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Abstract

This paper focuses on learning the hierarchical structure of a temporal scenario (for instance, a chord progression) to perform automatic improvisation consistently upon several time scales. We first present how to represent a hierarchical structure with a phrase structure grammar. Such a grammar enables us to analyse a scenario upon several levels of organisation creating a multi-level scenario. Then, we propose a method to automatically induce this grammar from a corpus based on sequence selection with mutual information. We applied this method on a corpus of rhythm changes and obtained multi-level scenarios similar to the analysis performed by a professional musician. We then propose new heuristics to exploit the multi-level structure of a scenario to guide the improvisation with anticipatory behaviour in the factor oracle driven improvisation paradigm. This method ensures consistency of the improvisation regarding the global form and opens up possibilities when playing on chords that do not exist in the memory. This system was evaluated by professional improvisers during listening sessions and received very good feedback.
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Dates and versions

hal-02378273 , version 1 (25-11-2019)

Identifiers

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Ken Déguernel, Emmanuel Vincent, Jérôme Nika, Gerard Assayag, Kamel Smaïli. Learning of Hierarchical Temporal Structures for Guided Improvisation. Computer Music Journal, 2019, 43 (2), ⟨10.1162/comj_a_00521⟩. ⟨hal-02378273⟩
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