Music Generation Using Bayesian Networks

• Published in: Machine Learning and Knowledge Discovery in Databases Vol. 10536; pp. 368 - 372
• Main Author: Kitahara, Tetsuro
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783319712727; 3319712721
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-71273-4_33

Music generation has recently become popular as an application of machine learning. To generate polyphonic music, one must consider both simultaneity (the vertical consistency) and sequentiality (the horizontal consistency). Bayesian networks are suitable to model both simultaneity and sequentiality simultaneously. Here, we present music generation models based on Bayesian networks applied to chord voicing, four-part harmonization, and real-time chord prediction.