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Machine Improvisation
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Journal Articles
Publisher: Journals Gateway
Computer Music Journal (2018) 42 (02): 35–51.
Published: 01 June 2018
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This article presents a machine-learning technique to analyze and produce statistical patterns in rhythm through real-time observation of human musicians. Here, timbre is considered an integral part of rhythm, as might be exemplified by hand-drum music. Moreover, this article considers challenges (such as mechanical timing delays, that are negligible in digitally synthesized music) that arise when the algorithm is executed on percussion robots. The algorithm's performance is analyzed in a variety of contexts, such as learning specific rhythms, learning a corpus of rhythms, responding to signal rhythms that signal musical transitions, improvising in different ways with a human partner, and matching the meter and the “syncopicity” of improvised music.
Journal Articles
Publisher: Journals Gateway
Computer Music Journal (2018) 42 (02): 52–66.
Published: 01 June 2018
Abstract
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This article presents two methods to generate automatic improvisation using training over multidimensional sequences. We consider musical features such as melody, harmony, timbre, etc., as dimensions. We first present a system combining interpolated probabilistic models with a factor oracle. The probabilistic models are trained on a corpus of musical work to learn the correlation between dimensions, and they are used to guide the navigation in the factor oracle to ensure a logical improvisation. Improvisations are therefore created in a way in which the intuition of a context is enriched with multidimensional knowledge. We then introduce a system creating multidimensional improvisations based on communication between dimensions via probabilistic message passing. The communication infers some anticipatory behavior on each dimension influenced by the others, creating a consistent multidimensional improvisation. Both systems were evaluated by professional improvisers during listening sessions. Overall, the systems received good feedback and showed encouraging results—first, on how multidimensional knowledge can improve navigation in the factor oracle and, second, on how communication through message passing can emulate the interactivity between dimensions or musicians.
Includes: Supplementary data