Machine Learning and Music Generation

Darrell C Conklin

ISBN 10: 0367892855 / ISBN 13: 9780367892852

Published by CRC Press Dez 2019, 2019

About this Item

Neuware - 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.

Seller Inventory # 9780367892852

Bibliographic Details

• Title: Machine Learning and Music Generation
• Publisher: CRC Press Dez 2019
• Publication Date: 2019
• Language: English
• ISBN 10: 0367892855
• ISBN 13: 9780367892852
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 242W x 170H x 6D millimeters 242 millimeters width by 170 millimeters height by 6 millimeters depth
• Weight: 177 grams

About this title

Synopsis

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.

About the Author

José M. Iñesta is a Professor in the Department of Software and Computing Systems at the Universidad de Alicante, Spain.

Darrell Conklin is a Professor in the Department of Computer Science and Artificial Intelligence at the University of the Basque Country.

Rafael Ramírez-Melendez is Associate Professor in the Music Technology Group in the Department of Information and Communication Technologies at the Universidad Pompeu Fabra, Barcelona, Spain.

Thomas M. Fiore is Associate Professor of Mathematics at the University of Michigan-Dearborn, MI, USA.