
Machine Learning and Music Generation
Routledge (Publisher)
1st Edition
Published on 18. December 2019
Book
Paperback/Softback
112 pages
978-0-367-89285-2 (ISBN)
Description
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.
More details
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Postgraduate and Undergraduate
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 242 mm
Width: 170 mm
Thickness: 6 mm
Weight
177 gr
ISBN-13
978-0-367-89285-2 (9780367892852)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Jose M. Inesta | Darrell C. Conklin | Rafael Ramirez-Melendez
Machine Learning and Music Generation
E-Book
10/2018
1st Edition
Routledge
€61.99
Available for download

Jose M. Inesta | Darrell C. Conklin | Rafael Ramirez-Melendez
Machine Learning and Music Generation
E-Book
10/2018
1st Edition
Routledge
€61.99
Available for download

Jose M. Inesta | Darrell C. Conklin | Rafael Ramirez-Melendez
Machine Learning and Music Generation
Book
12/2017
1st Edition
Routledge
€222.84
Shipment within 15-20 days
Persons
Jose M. Inesta 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 Ramirez-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.
Darrell Conklin is a Professor in the Department of Computer Science and Artificial Intelligence at the University of the Basque Country.
Rafael Ramirez-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.
Content
Introduction: Machine learning and music generation 1. Chord sequence generation with semiotic patterns 2. A machine learning approach to ornamentation modeling and synthesis in jazz guitar 3. Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis 4. Mapping between dynamic markings and performed loudness: a machine learning approach 5. Data-based melody generation through multi-objective evolutionary computation