Abstract
In this paper, we propose a method of generating walking bass lines for jazz with a hidden Markov model (HMM). Although automatic harmonization has been widely and actively studied, automatic generation of walking bass lines has not. With our model, which includes hidden states that represent combinations of pitch classes and metric positions, different distributions of bass notes selected at different metric positions can be learned. The results of objective and subjective evaluations suggest that the model can learn such different tendencies of bass notes at different metric positions and generates musically flowing bass lines that contain passing notes.
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Acknowledgments
This project was supported by JSPS Kakenhi (JP16K16180, JP16H01744, JP17H00749, and JP19K12288) and the Kawai Foundation for Sound Technology and Music.
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Shiga, A., Kitahara, T. (2021). Generating Walking Bass Lines with HMM. In: Kronland-Martinet, R., Ystad, S., Aramaki, M. (eds) Perception, Representations, Image, Sound, Music. CMMR 2019. Lecture Notes in Computer Science(), vol 12631. Springer, Cham. https://doi.org/10.1007/978-3-030-70210-6_17
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DOI: https://doi.org/10.1007/978-3-030-70210-6_17
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