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Handbook of Artificial Intelligence for Music

Overview of attention for book
Cover of 'Handbook of Artificial Intelligence for Music'

Table of Contents

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    Book Overview
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    Chapter 1 Sociocultural and Design Perspectives on AI-Based Music Production: Why Do We Make Music and What Changes if AI Makes It for Us?
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    Chapter 2 Human–Machine Simultaneity in the Compositional Process
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    Chapter 3 Artificial Intelligence for Music Composition
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    Chapter 4 Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry
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    Chapter 5 Neuroscience of Musical Improvisation
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    Chapter 6 Discovering the Neuroanatomical Correlates of Music with Machine Learning
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    Chapter 7 Music, Artificial Intelligence and Neuroscience
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    Chapter 8 Creative Music Neurotechnology
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    Chapter 9 On Making Music with Heartbeats
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    Chapter 10 Cognitive Musicology and Artificial Intelligence: Harmonic Analysis, Learning, and Generation
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    Chapter 11 On Modelling Harmony with Constraint Programming for Algorithmic Composition Including a Model of Schoenberg’s Theory of Harmony
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    Chapter 12 Constraint-Solving Systems in Music Creation
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    Chapter 13 AI Music Mixing Systems
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    Chapter 14 Machine Improvisation in Music: Information-Theoretical Approach
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    Chapter 15 Structure, Abstraction and Reference in Artificial Musical Intelligence
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    Chapter 16 Folk the Algorithms: (Mis)Applying Artificial Intelligence to Folk Music
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    Chapter 17 Automatic Music Composition with Evolutionary Algorithms: Digging into the Roots of Biological Creativity
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    Chapter 18 Assisted Music Creation with Flow Machines: Towards New Categories of New
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    Chapter 19 Performance Creativity in Computer Systems for Expressive Performance of Music
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    Chapter 20 Imitative Computer-Aided Musical Orchestration with Biologically Inspired Algorithms
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    Chapter 21 Human-Centred Artificial Intelligence in Concatenative Sound Synthesis
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    Chapter 22 Deep Generative Models for Musical Audio Synthesis
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    Chapter 23 Transfer Learning for Generalized Audio Signal Processing
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    Chapter 24 From Audio to Music Notation
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    Chapter 25 Automatic Transcription of Polyphonic Vocal Music
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    Chapter 26 Graph-Based Representation, Analysis, and Interpretation of Popular Music Lyrics Using Semantic Embedding Features
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    Chapter 27 Interactive Machine Learning of Musical Gesture
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    Chapter 28 Human–Robot Musical Interaction
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    Chapter 29 Shimon Sings-Robotic Musicianship Finds Its Voice
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    Chapter 30 AI-Lectronica: Music AI in Clubs and Studio Production
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    Chapter 31 Musicking with Algorithms: Thoughts on Artificial Intelligence, Creativity, and Agency
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    Chapter 32 cellF: Surrogate Musicianship as a Manifestation of In-Vitro Intelligence
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    Chapter 33 On Growing Computers from Living Biological Cells
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    Chapter 34 Quantum Computer: Hello, Music!
Overall attention for this book and its chapters
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Mentioned by

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68 X users
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1 patent
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2 Wikipedia pages

Citations

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22 Dimensions

Readers on

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48 Mendeley
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Title
Handbook of Artificial Intelligence for Music
Published by
Springer International Publishing, August 2021
DOI 10.1007/978-3-030-72116-9
ISBNs
978-3-03-072115-2, 978-3-03-072116-9
Editors

Eduardo Reck Miranda

X Demographics

X Demographics

The data shown below were collected from the profiles of 68 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 48 100%

Demographic breakdown

Readers by professional status Count As %
Lecturer > Senior Lecturer 4 8%
Student > Ph. D. Student 3 6%
Student > Master 3 6%
Lecturer 2 4%
Student > Doctoral Student 2 4%
Other 8 17%
Unknown 26 54%
Readers by discipline Count As %
Arts and Humanities 10 21%
Computer Science 4 8%
Unspecified 2 4%
Biochemistry, Genetics and Molecular Biology 1 2%
Business, Management and Accounting 1 2%
Other 4 8%
Unknown 26 54%