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.
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.
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.
Inhaltsangabe
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
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
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