Computational approaches to music composition and style imitation have engaged musicians, music scholars, and computer scientists since the early days of computing. Music generation research has generally employed one of two strategies: knowledge-based methods that model style through explicitly formalized rules, and data mining methods that apply machine learning to induce statistical models of musical style. The five chapters in this book illustrate the range of tasks and design choices in current music generation research applying machine learning techniques and highlighting recurring research issues such as training data, music representation, candidate generation, and evaluation. The contributions focus on different aspects of modeling and generating music, including melody, chord sequences, ornamentation, and dynamics. Models are induced from audio data or symbolic data. This book was originally published as a special issue of the Journal of Mathematics and Music.
Introduction:Machine learning and music generation José M. Iñesta, Darrell Conklin, and Rafael Ramírez
1. Chord sequence generation with semiotic patterns Darrell Conklin
2. A machine learning approach to ornamentation modeling and synthesis in jazz guitar Sergio Giraldo and Rafael Ramírez
3. Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis Phillip B. Kirlin and Jason Yust
4. Mapping between dynamic markings and performed loudness: a machine learning approach Katerina Kosta, Rafael Ramírez, Oscar F. Bandtlow, and Elaine Chew
5. Data-based melody generation through multi-objective evolutionary computation Pedro J. Ponce de León, José M. Iñesta, Jorge Calvo-Zaragoza, and David Rizo