Deep and Shallow

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.

• PrefaceChapter 1 Introduction to Sounds of MusicChapter 2 Noise: the Hidden Dynamics of MusicChapter 3 Communicating Musical InformationChapter 4 Understanding and (Re)Creating Sound Chapter 5 Generating and Listening to Audio InformationChapter 6 Artificial Musical BrainsChapter 7 Representing Voices in Pitch and TimeChapter 8 Noise Revisited: Brains that ImagineChapter 9 Paying (Musical) AttentionChapter 10 Last Noisy Thoughts, Summary and ConclusionAppendix A Introduction to Neural Network Frameworks: Keras, Tensorflow, PytorchAppendix B Summary of Programming Examples and ExercisesAppendix C Software Packages for Music and Audio Representation and AnalysisAppendix D Free Music and Audio Editting SoftwareAppendix E DatasetsAppendix F Figure AttributionsReferences Index

"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