音楽と音響の機械学習 Deep and Shallow : Machine Learning in Music and Audio

著者名：Dubnov, Shlomo/Greer, Ross

Chapman and Hall/CRC（2023/12/08発売）

• 言語：ENG
• ISBN：9781032146188
• eISBN：9781000984538

Description

Providing an essential and unique bridge between the theories of signal processing, machine learning, and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place, this book explores how sound may be represented and manipulated by computer systems, and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory.

Introducing popular fundamental ideas in AI at a comfortable pace, more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory, without the frustrations of free-form coding.

Surveying state-of-the art methods in applications of deep neural networks to audio and sound computing, as well as offering a research perspective that suggests future challenges in music and AI research, this book appeals to both students of AI and music, as well as industry professionals in the fields of machine learning, music, and AI.

Table of Contents

Preface

Chapter 1 Introduction to Sounds of Music

Chapter 2 Noise: the Hidden Dynamics of Music

Chapter 3 Communicating Musical Information

Chapter 4 Understanding and (Re)Creating Sound

Chapter 5 Generating and Listening to Audio Information

Chapter 6 Artificial Musical Brains

Chapter 7 Representing Voices in Pitch and Time

Chapter 8 Noise Revisited: Brains that Imagine

Chapter 9 Paying (Musical) Attention

Chapter 10 Last Noisy Thoughts, Summary and Conclusion

Appendix A Introduction to Neural Network Frameworks: Keras, Tensorflow, Pytorch

Appendix B Summary of Programming Examples and Exercises

Appendix C Software Packages for Music and Audio Representation and Analysis

Appendix D Free Music and Audio Editting Software

Appendix E Datasets

Appendix F Figure Attributions

References

Index