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Author: Stephan M. Schwanauer
Publisher: MIT Press
ISBN: 9780262193191
Category : Computers
Languages : en
Pages : 572
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Book Description
Machine Models of Music brings together representative models and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research. Machine Models of Music brings together representative models ranging from Mozart's "Musical Dice Game" to a classic article by Marvin Minsky and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research.Major sections of the book take up pioneering research in generate-and-test composition (Lejaren Hiller, Barry Brooks, Jr., Stanley Gill); composition parsing (Allen Forte, Herbert Simon, Terry Winograd); heuristic composition (John Rothgeb, James Moorer, Steven Smoliar); generative grammars (Otto Laske, Gary Rader, Johan Sundberg, Fred Lerdahl); alternative theories (Marvin Minsky, James Meehan); composition tools (Charles Ames, Kemal Ebcioglu, David Cope, C. Fry); and new directions (David Levitt, Christopher Longuet-Higgins, Jamshed Bharucha, Stephan Schwanauer).Stephan Schwanauer is President of Mediasoft Corporation. David Levitt is the founder of HIP Software and head of audio products at VPL Research.
Author: Stephan M. Schwanauer
Publisher: MIT Press
ISBN: 9780262193191
Category : Computers
Languages : en
Pages : 572
Get Book Here
Book Description
Machine Models of Music brings together representative models and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research. Machine Models of Music brings together representative models ranging from Mozart's "Musical Dice Game" to a classic article by Marvin Minsky and current research to illustrate the rich impact that artificial intelligence has had on the understanding and composition of traditional music and to demonstrate the ways in which music can push the boundaries of traditional Al research.Major sections of the book take up pioneering research in generate-and-test composition (Lejaren Hiller, Barry Brooks, Jr., Stanley Gill); composition parsing (Allen Forte, Herbert Simon, Terry Winograd); heuristic composition (John Rothgeb, James Moorer, Steven Smoliar); generative grammars (Otto Laske, Gary Rader, Johan Sundberg, Fred Lerdahl); alternative theories (Marvin Minsky, James Meehan); composition tools (Charles Ames, Kemal Ebcioglu, David Cope, C. Fry); and new directions (David Levitt, Christopher Longuet-Higgins, Jamshed Bharucha, Stephan Schwanauer).Stephan Schwanauer is President of Mediasoft Corporation. David Levitt is the founder of HIP Software and head of audio products at VPL Research.
Author: José M. Iñesta
Publisher: Routledge
ISBN: 1351234528
Category : Mathematics
Languages : en
Pages : 153
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Book 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.
Author: José M. Iñesta
Publisher: Routledge
ISBN: 1351234536
Category : Mathematics
Languages : en
Pages : 112
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Book 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.
Author: Eduardo Reck Miranda
Publisher: A-R Editions, Inc.
ISBN: 9780895796738
Category : Music
Languages : en
Pages : 334
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Book Description
Artificial Life, or A-Life, aims at the study of all phenomena characteristic of natural living systems, through computational modeling, wetware-hardware hybrids, and other artificial media. Its scope ranges from the investigation of the emergence of cognitive processes in natural or artificial systems to the development of life or life-like properties from inorganic components. A number of musicians, in particular composers and musicologists, have started to turn to A-Life for inspiration and working methodology. This edited volume features thirteen chapters written by researchers and practitioners in this exciting emerging field of computer music, and includes a CD with various examples music related to A-Life.
Author: Shlomo Dubnov
Publisher: CRC Press
ISBN: 1000984532
Category : Computers
Languages : en
Pages : 430
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Book 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.
Author: Jean-Pierre Briot
Publisher: Springer
ISBN: 3319701630
Category : Computers
Languages : en
Pages : 284
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Book Description
This book is a survey and analysis of how deep learning can be used to generate musical content. The authors offer a comprehensive presentation of the foundations of deep learning techniques for music generation. They also develop a conceptual framework used to classify and analyze various types of architecture, encoding models, generation strategies, and ways to control the generation. The five dimensions of this framework are: objective (the kind of musical content to be generated, e.g., melody, accompaniment); representation (the musical elements to be considered and how to encode them, e.g., chord, silence, piano roll, one-hot encoding); architecture (the structure organizing neurons, their connexions, and the flow of their activations, e.g., feedforward, recurrent, variational autoencoder); challenge (the desired properties and issues, e.g., variability, incrementality, adaptability); and strategy (the way to model and control the process of generation, e.g., single-step feedforward, iterative feedforward, decoder feedforward, sampling). To illustrate the possible design decisions and to allow comparison and correlation analysis they analyze and classify more than 40 systems, and they discuss important open challenges such as interactivity, originality, and structure. The authors have extensive knowledge and experience in all related research, technical, performance, and business aspects. The book is suitable for students, practitioners, and researchers in the artificial intelligence, machine learning, and music creation domains. The reader does not require any prior knowledge about artificial neural networks, deep learning, or computer music. The text is fully supported with a comprehensive table of acronyms, bibliography, glossary, and index, and supplementary material is available from the authors' website.
Author: Alan Marsden
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 328
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Book Description
A collection of papers from a recent international conference concerned with computers in music research. The selection presents detailed discussions of computational representations and models in music, and aims to lay the foundations for future music software.
Author: David Cope
Publisher: Computer Models of Musical Creativity
ISBN: 9780262534109
Category : Composition (Music)
Languages : en
Pages : 474
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Book Description
"He then describes a model that integrates these different aspects - an inductive-association computational process that can create music. Cope's writing style is lively and nontechnical; the reader needs neither knowledge of computer programming nor specialized computer hardware or software to follow the text."--Jacket.
Author: Peter Desain
Publisher:
ISBN:
Category : Music
Languages : en
Pages : 348
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Book Description
Author: Gerhard Nierhaus
Publisher: Springer Science & Business Media
ISBN: 3211755403
Category : Mathematics
Languages : en
Pages : 294
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Book Description
Algorithmic composition – composing by means of formalizable methods – has a century old tradition not only in occidental music history. This is the first book to provide a detailed overview of prominent procedures of algorithmic composition in a pragmatic way rather than by treating formalizable aspects in single works. In addition to an historic overview, each chapter presents a specific class of algorithm in a compositional context by providing a general introduction to its development and theoretical basis and describes different musical applications. Each chapter outlines the strengths, weaknesses and possible aesthetical implications resulting from the application of the treated approaches. Topics covered are: markov models, generative grammars, transition networks, chaos and self-similarity, genetic algorithms, cellular automata, neural networks and artificial intelligence are covered. The comprehensive bibliography makes this work ideal for the musician and the researcher alike.
