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Publication
Phrase-oriented generative rhythmic patterns for jazz solos
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Authors
Advisor(s)
Abstract(s)
This study introduces a novel generative approach for crafting phrase-oriented rhythmic patterns in jazz solos, leveraging statistical analyses of a comprehensive corpus, the Weimar Jazz Database. Jazz solos, celebrated for their improvisational complexity, require a delicate interplay between rhythm and melody, making the generation of authentic rhythmic patterns a challenging task. This work systematically explores the relationships among rhythmic elements, including phrases, beats, divisions, and patterns. The generative method employs a Markov chain framework to synthesize rhythmic divisions and patterns,
ensuring stylistic coherence and diversity. An extensive evaluation compares original and generated datasets through statistical and machine learning metrics, validating the generative model’s ability to replicate key rhythmic characteristics while fostering innovation. The findings underscore the potential of this approach to contribute significantly to the fields of computational creativity and algorithmic music composition, providing a robust tool for generating compelling jazz solos.
Description
The authors would like to thank Klaus Frieler for granting access to the dataset and for his valuable suggestions, which greatly contributed to the development of this work.
Keywords
Generative music Machine learning Algorithmic composition Statistical methods Rhythmic patterns Jazz solos
Pedagogical Context
Citation
RAPOSO, A.N. ; SOARES, V.N.G.J. (2025) - Phrase-oriented generative rhythmic patterns for jazz solos. Appl. Sci. 15, p. 11058. DOI: 10.3390/app152011058
Publisher
MDPI