1st Edition
Deep and Shallow Machine Learning in Music and Audio
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
Biography
Shlomo Dubnov is a Professor in the Music Department and Affiliate Professor in Computer Science and Engineering at the University of California, San Diego. He is best known for his research on poly-spectral analysis of musical timbre and inventing the method of Music Information Dynamics with applications in Computer Audition and Machine improvisation. His previous books on The Structure of Style: Algorithmic Approaches to Understanding Manner and Meaning and Cross-Cultural Multimedia Computing: Semantic and Aesthetic Modeling were published by Springer.
Ross Greer is a PhD Candidate in Electrical & Computer Engineering at the University of California, San Diego, where he conducts research at the intersection of artificial intelligence and human agent interaction. Beyond exploring technological approaches to musical expression, Ross creates music as a conductor and orchestrator for instrumental ensembles. Ross received his B.S. and B.A. degrees in EECS, Engineering Physics, and Music from UC Berkeley, and an M.S. in Electrical & Computer Engineering from UC San Diego.
"Deep and Shallow by Shlomo Dubnov and Ross Greer is an exceptional journey into the convergence of music, artificial intelligence, and signal processing. Seamlessly weaving together intricate theories with practical programming activities, the book guides readers, whether novices or experts, toward a profound understanding of how AI can reshape musical creativity. A true gem for both enthusiasts and professionals, this book eloquently bridges the gap between foundational concepts of music information dynamics as an underlying basis for understanding music structure and listening experience, and cutting-edge applications, ushering us into the future of music and AI with clarity and excitement."
Gil Weinberg, Professor and Founding Director, Georgia Tech Center for Music Technology
"The authors make an enormous contribution, not only as a textbook, but as essential reading on music information dynamics, bridging multiple disciplines of music, information theory, and machine learning. The theory is illustrated and grounded in plenty of practical information and resources."
Roger B. Dannenberg, Emeritus Professor of Computer Science, Art & Music, Carnegie Mellon University
