Work

Phrase-Oriented Generative Rhythmic Patterns for Jazz Solos

Year: 2025

Type: article

Abstract: 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.

Source: Applied Sciences

DOI: 10.3390/app152011058

Authors: Adriano N. Raposo, Vasco N. G. J. Soares

Institutions: Universidade Politécnica de Castelo Branco, University of Beira Interior, Instituto de Telecomunicações

Language: English

Topic: Music Technology and Sound Studies

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funders: Instituto de Telecomunicações, Association Neurofibromatoses et Recklinghause, Fundação para a Ciência e a Tecnologia