Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2021
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| Schlagworte: | |
| Online-Zugang: | BSB01 |
| Beschreibung: | 1 Online-Ressource (1007 Seiten) |
| ISBN: | 9783030721169 |
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| 245 | 1 | 0 | |a Handbook of Artificial Intelligence for Music |b Foundations, Advanced Approaches, and Developments for Creativity |
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| 505 | 8 | |a Intro -- Foreword: From Audio Signals to Musical Meaning -- References -- Preface -- Contents -- Editor and Contributors -- 1 Sociocultural and Design Perspectives on AI-Based Music Production: Why Do We Make Music and What Changes if AI Makes It for Us? -- 1.1 Introduction -- 1.2 The Philosophical Era -- 1.3 Creative Cognition and Lofty Versus Lowly Computational Creativity -- 1.4 The Design Turn -- 1.4.1 Design Evaluation -- 1.5 The Sociological View -- 1.5.1 Cluster Concepts and Emic Versus Etic Definitions -- 1.5.2 Social Perspectives on the Psychology of Creativity -- 1.5.3 Social Theories of Taste and Identity -- 1.5.4 Why Do We Make and Listen to Music? -- 1.6 Discussion -- 2 Human-Machine Simultaneity in the Compositional Process -- 2.1 Introduction -- 2.2 Machine as Projection Space -- 2.3 Temporal Interleaving -- 2.4 Work -- 2.5 Artistic Research -- 2.6 Suspension -- 3 Artificial Intelligence for Music Composition -- 3.1 Introduction -- 3.2 Artificial Intelligence and Distributed Human-Computer Co-creativity -- 3.3 Machine Learning: Applications in Music and Compositional Potential -- 3.3.1 Digital Musical Instruments -- 3.3.2 Interactive Music Systems -- 3.3.3 Computational Aesthetic Evaluation -- 3.3.4 Human-Computer Co-exploration -- 3.4 Conceptual Considerations -- 3.4.1 The Computer as a Compositional Prosthesis -- 3.4.2 The Computer as a Virtual Player -- 3.4.3 Artificial Intelligence as a Secondary Agent -- 3.5 Limitations of Machine Learning -- 3.6 Composition and AI: The Road Ahead -- Acknowledgements -- References -- 4 Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry -- 4.1 Introduction -- 4.2 Combining Art, Science and Sound Research -- 4.2.1 Practice-Based Research and Objective Knowledge -- 4.2.2 Artistic Intervention in Scientific Research | |
| 505 | 8 | |a 4.3 Machine Learning as a Tool for Musical Performance -- 4.3.1 Corpus Nil -- 4.3.2 Scientific and Artistic Drives -- 4.3.3 Development and Observations -- 4.4 Artificial Intelligence as Actor in Performance -- 4.4.1 Humane Methods -- 4.4.2 Scientific and Artistic Drives -- 4.4.3 Development and Observations -- 4.5 Discussion -- 4.5.1 Artificial Intelligence and Music -- 4.5.2 From Machine Learning to Artificial Intelligence -- 4.5.3 Hybrid Methodology -- 5 Neuroscience of Musical Improvisation -- 5.1 Introduction -- 5.2 Cognitive Neuroscience of Music -- 5.3 Intrinsic Networks of the Brain -- 5.4 Temporally Precise Indices of Brain Activity in Music -- 5.5 Attention Toward Moments in Time -- 5.6 Prediction and Reward -- 5.7 Music and Language Learning -- 5.8 Conclusions: Creativity at Multiple Levels -- References -- 6 Discovering the Neuroanatomical Correlates of Music with Machine Learning -- 6.1 Introduction -- 6.2 Brain and Statistical Learning Machine -- 6.2.1 Prediction and Entropy Encoding -- 6.2.2 Learning -- 6.2.2.1 Timbre, Phoneme, and Pitch: Distributional Learning -- 6.2.2.2 Chunk and Word: Transitional Probability -- 6.2.2.3 Syntax and Grammar: Local Versus Non-local Dependencies -- 6.2.3 Memory -- 6.2.3.1 Semantic Versus Episodic -- 6.2.3.2 Short-Term Versus Long-Term -- 6.2.3.3 Consolidation -- 6.2.4 Action and Production -- 6.2.5 Social Communication -- 6.3 Computational Model -- 6.3.1 Mathematical Concepts of the Brain's Statistical Learning -- 6.3.2 Statistical Learning and the Neural Network -- 6.4 Neurobiological Model -- 6.4.1 Temporal Mechanism -- 6.4.2 Spatial Mechanism -- 6.4.2.1 Domain Generality Versus Domain Specificity -- 6.4.2.2 Probability Encoding -- 6.4.2.3 Uncertainty Encoding -- 6.4.2.4 Consolidation of Statistically Learned Knowledge -- 6.5 Future Direction: Creativity | |
| 505 | 8 | |a 6.5.1 Optimization for Creativity Rather than Efficiency -- 6.5.2 Cognitive Architectures -- 6.5.3 Neuroanatomical Correlates -- 6.5.3.1 Frontal Lobe -- 6.5.3.2 Cerebellum -- 6.5.3.3 Neural Network -- 6.6 Concluding Remarks -- Acknowledgements -- References -- 7 Music, Artificial Intelligence and Neuroscience -- 7.1 Introduction -- 7.2 Music -- 7.3 Artificial Intelligence -- 7.4 Neuroscience -- 7.5 Music and Neuroscience -- 7.6 Artificial Intelligence and Neuroscience -- 7.7 Music and Artificial Intelligence -- 7.8 Music, AI, and Neuroscience: A Test -- 7.9 Concluding Discussion -- References -- 8 Creative Music Neurotechnology -- 8.1 Introduction -- 8.2 Sound Synthesis with Real Neuronal Networks -- 8.3 Raster Plot: Making Music with Spiking Neurones -- 8.4 Symphony of Minds Listening: Listening to the Listening Mind -- 8.4.1 Brain Scanning and Analysis -- 8.4.2 The Compositional Process -- 8.4.3 The Musical Engine: MusEng -- 8.4.3.1 Learning Phase -- 8.4.3.2 Generative Phase -- 8.4.3.3 Transformative Phase -- Pitch Inversion Algorithm -- Pitch Scrambling Algorithm -- 8.5 Brain-Computer Music Interfacing -- 8.5.1 ICCMR's First SSVEP-Based BCMI System -- 8.5.2 Activating Memory and The Paramusical Ensemble -- 8.6 Concluding Discussion and Acknowledgements -- Acknowledgements -- Appendix: Two Pages of Raster Plot -- References -- 9 On Making Music with Heartbeats -- 9.1 Introduction -- 9.1.1 Why Cardiac Arrhythmias -- 9.1.2 Why Music Representation -- 9.1.3 Hearts Driving Music -- 9.2 Music Notation in Cardiac Auscultation -- 9.2.1 Venous Hum -- 9.2.2 Heart Murmurs -- 9.3 Music Notation of Cardiac Arrhythmias -- 9.3.1 Premature Ventricular and Atrial Contractions -- 9.3.2 A Theory of Beethoven and Arrhythmia -- 9.3.3 Ventricular and Supraventricular Tachycardias -- 9.3.4 Atrial Fibrillation -- 9.3.5 Atrial Flutter | |
| 505 | 8 | |a 9.4 Music Generation from Abnormal Heartbeats -- 9.4.1 A Retrieval Task -- 9.4.2 A Matter of Transformation -- 9.5 Conclusions and Discussion -- 10 Cognitive Musicology and Artificial Intelligence: Harmonic Analysis, Learning, and Generation -- 10.1 Introduction -- 10.2 Classical Artificial Intelligence Versus Deep Learning -- 10.3 Melodic Harmonization: Symbolic and Subsymbolic Models -- 10.4 Inventing New Concepts: Conceptual Blending in Harmony -- 10.5 Conclusions -- References -- 11 On Modelling Harmony with Constraint Programming for Algorithmic Composition Including a Model of Schoenberg's Theory of Harmony -- 11.1 Introduction -- 11.2 Application Examples -- 11.2.1 Automatic Melody Harmonisation -- 11.2.2 Modelling Schoenberg's Theory of Harmony -- 11.2.3 A Compositional Application in Extended Tonality -- 11.3 Overview: Constraint Programming for Modelling Harmony -- 11.3.1 Why Constraint Programming for Music Composition? -- 11.3.2 What Is Constraint Programming? -- 11.3.3 Music Constraint Systems for Algorithmic Composition -- 11.3.4 Harmony Modelling -- 11.3.5 Constraint-Based Harmony Systems -- 11.4 Case Study: A Constraint-Based Harmony Framework -- 11.4.1 Declaration of Chord and Scale Types -- 11.4.2 Temporal Music Representation -- 11.4.3 Chords and Scales -- 11.4.4 Notes with Analytical Information -- 11.4.5 Degrees, Accidentals and Enharmonic Spelling -- 11.4.6 Efficient Search with Constraint Propagation -- 11.4.7 Implementation -- 11.5 An Example: Modelling Schoenberg's Theory of Harmony -- 11.5.1 Score Topology -- 11.5.2 Pitch Resolution -- 11.5.3 Chord Types -- 11.5.4 Part Writing Rules -- 11.5.5 Simplified Root Progression Directions: Harmonic Band -- 11.5.6 Chord Inversions -- 11.5.7 Refined Root Progression Rules -- 11.5.8 Cadences -- 11.5.9 Dissonance Treatment -- 11.5.10 Modulation -- 11.6 Discussion | |
| 505 | 8 | |a 11.6.1 Comparison with Previous Systems -- 11.6.2 Limitations of the Framework -- 11.6.3 Completeness of Schoenberg Model -- 11.7 Future Research -- 11.7.1 Supporting Musical Form with Harmony -- 11.7.2 Combining Rule-Based Composition with Machine Learning -- 11.8 Summary -- 12 Constraint-Solving Systems in Music Creation -- 12.1 Introduction -- 12.2 Early Rule Formalizations for Computer-Generated Music -- 12.3 Improving Your Chances -- 12.4 Making Room for Exceptions -- 12.5 The Musical Challenge -- 12.6 Opening up for Creativity -- 12.7 The Need for Higher Efficiency -- 12.8 OMRC - greaterthan PWMC - greaterthan ClusterEngine -- 12.8.1 Musical Potential -- 12.8.2 Challenging Order -- 12.8.3 An Efficient User Interface -- 12.9 Future Developments and Final Remarks -- References -- 13 AI Music Mixing Systems -- 13.1 Introduction -- 13.2 Decision-Making Process -- 13.2.1 Knowledge Encoding -- 13.2.2 Expert Systems -- 13.2.3 Data Driven -- 13.2.4 Decision-Making Summary -- 13.3 Audio Manipulation -- 13.3.1 Adaptive Audio Effects -- 13.3.2 Direct Transformation -- 13.3.3 Audio Manipulation Summary -- 13.4 Human-Computer Interaction -- 13.4.1 Automatic -- 13.4.2 Independent -- 13.4.3 Recommendation -- 13.4.4 Discovery -- 13.4.5 Control-Level Summary -- 13.5 Further Design Considerations -- 13.5.1 Mixing by Sub-grouping -- 13.5.2 Intelligent Mixing Systems in Context -- 13.6 Discussion -- 13.7 The Future of Intelligent Mixing Systems -- 14 Machine Improvisation in Music: Information-Theoretical Approach -- 14.1 What Is Machine Improvisation -- 14.2 How It All Started: Motivation and Theoretical Setting -- 14.2.1 Part 1: Stochastic Modeling, Prediction, Compression, and Entropy -- 14.3 Generation of Music Sequences Using Lempel-Ziv (LZ) -- 14.3.1 Incremental Parsing -- 14.3.2 Generative Model Based on LZ. | |
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| author | Miranda, Eduardo Reck |
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| bvnumber | BV048935494 |
| collection | ZDB-1-PQM ZDB-30-PQE |
| contents | Intro -- Foreword: From Audio Signals to Musical Meaning -- References -- Preface -- Contents -- Editor and Contributors -- 1 Sociocultural and Design Perspectives on AI-Based Music Production: Why Do We Make Music and What Changes if AI Makes It for Us? -- 1.1 Introduction -- 1.2 The Philosophical Era -- 1.3 Creative Cognition and Lofty Versus Lowly Computational Creativity -- 1.4 The Design Turn -- 1.4.1 Design Evaluation -- 1.5 The Sociological View -- 1.5.1 Cluster Concepts and Emic Versus Etic Definitions -- 1.5.2 Social Perspectives on the Psychology of Creativity -- 1.5.3 Social Theories of Taste and Identity -- 1.5.4 Why Do We Make and Listen to Music? -- 1.6 Discussion -- 2 Human-Machine Simultaneity in the Compositional Process -- 2.1 Introduction -- 2.2 Machine as Projection Space -- 2.3 Temporal Interleaving -- 2.4 Work -- 2.5 Artistic Research -- 2.6 Suspension -- 3 Artificial Intelligence for Music Composition -- 3.1 Introduction -- 3.2 Artificial Intelligence and Distributed Human-Computer Co-creativity -- 3.3 Machine Learning: Applications in Music and Compositional Potential -- 3.3.1 Digital Musical Instruments -- 3.3.2 Interactive Music Systems -- 3.3.3 Computational Aesthetic Evaluation -- 3.3.4 Human-Computer Co-exploration -- 3.4 Conceptual Considerations -- 3.4.1 The Computer as a Compositional Prosthesis -- 3.4.2 The Computer as a Virtual Player -- 3.4.3 Artificial Intelligence as a Secondary Agent -- 3.5 Limitations of Machine Learning -- 3.6 Composition and AI: The Road Ahead -- Acknowledgements -- References -- 4 Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry -- 4.1 Introduction -- 4.2 Combining Art, Science and Sound Research -- 4.2.1 Practice-Based Research and Objective Knowledge -- 4.2.2 Artistic Intervention in Scientific Research 4.3 Machine Learning as a Tool for Musical Performance -- 4.3.1 Corpus Nil -- 4.3.2 Scientific and Artistic Drives -- 4.3.3 Development and Observations -- 4.4 Artificial Intelligence as Actor in Performance -- 4.4.1 Humane Methods -- 4.4.2 Scientific and Artistic Drives -- 4.4.3 Development and Observations -- 4.5 Discussion -- 4.5.1 Artificial Intelligence and Music -- 4.5.2 From Machine Learning to Artificial Intelligence -- 4.5.3 Hybrid Methodology -- 5 Neuroscience of Musical Improvisation -- 5.1 Introduction -- 5.2 Cognitive Neuroscience of Music -- 5.3 Intrinsic Networks of the Brain -- 5.4 Temporally Precise Indices of Brain Activity in Music -- 5.5 Attention Toward Moments in Time -- 5.6 Prediction and Reward -- 5.7 Music and Language Learning -- 5.8 Conclusions: Creativity at Multiple Levels -- References -- 6 Discovering the Neuroanatomical Correlates of Music with Machine Learning -- 6.1 Introduction -- 6.2 Brain and Statistical Learning Machine -- 6.2.1 Prediction and Entropy Encoding -- 6.2.2 Learning -- 6.2.2.1 Timbre, Phoneme, and Pitch: Distributional Learning -- 6.2.2.2 Chunk and Word: Transitional Probability -- 6.2.2.3 Syntax and Grammar: Local Versus Non-local Dependencies -- 6.2.3 Memory -- 6.2.3.1 Semantic Versus Episodic -- 6.2.3.2 Short-Term Versus Long-Term -- 6.2.3.3 Consolidation -- 6.2.4 Action and Production -- 6.2.5 Social Communication -- 6.3 Computational Model -- 6.3.1 Mathematical Concepts of the Brain's Statistical Learning -- 6.3.2 Statistical Learning and the Neural Network -- 6.4 Neurobiological Model -- 6.4.1 Temporal Mechanism -- 6.4.2 Spatial Mechanism -- 6.4.2.1 Domain Generality Versus Domain Specificity -- 6.4.2.2 Probability Encoding -- 6.4.2.3 Uncertainty Encoding -- 6.4.2.4 Consolidation of Statistically Learned Knowledge -- 6.5 Future Direction: Creativity 6.5.1 Optimization for Creativity Rather than Efficiency -- 6.5.2 Cognitive Architectures -- 6.5.3 Neuroanatomical Correlates -- 6.5.3.1 Frontal Lobe -- 6.5.3.2 Cerebellum -- 6.5.3.3 Neural Network -- 6.6 Concluding Remarks -- Acknowledgements -- References -- 7 Music, Artificial Intelligence and Neuroscience -- 7.1 Introduction -- 7.2 Music -- 7.3 Artificial Intelligence -- 7.4 Neuroscience -- 7.5 Music and Neuroscience -- 7.6 Artificial Intelligence and Neuroscience -- 7.7 Music and Artificial Intelligence -- 7.8 Music, AI, and Neuroscience: A Test -- 7.9 Concluding Discussion -- References -- 8 Creative Music Neurotechnology -- 8.1 Introduction -- 8.2 Sound Synthesis with Real Neuronal Networks -- 8.3 Raster Plot: Making Music with Spiking Neurones -- 8.4 Symphony of Minds Listening: Listening to the Listening Mind -- 8.4.1 Brain Scanning and Analysis -- 8.4.2 The Compositional Process -- 8.4.3 The Musical Engine: MusEng -- 8.4.3.1 Learning Phase -- 8.4.3.2 Generative Phase -- 8.4.3.3 Transformative Phase -- Pitch Inversion Algorithm -- Pitch Scrambling Algorithm -- 8.5 Brain-Computer Music Interfacing -- 8.5.1 ICCMR's First SSVEP-Based BCMI System -- 8.5.2 Activating Memory and The Paramusical Ensemble -- 8.6 Concluding Discussion and Acknowledgements -- Acknowledgements -- Appendix: Two Pages of Raster Plot -- References -- 9 On Making Music with Heartbeats -- 9.1 Introduction -- 9.1.1 Why Cardiac Arrhythmias -- 9.1.2 Why Music Representation -- 9.1.3 Hearts Driving Music -- 9.2 Music Notation in Cardiac Auscultation -- 9.2.1 Venous Hum -- 9.2.2 Heart Murmurs -- 9.3 Music Notation of Cardiac Arrhythmias -- 9.3.1 Premature Ventricular and Atrial Contractions -- 9.3.2 A Theory of Beethoven and Arrhythmia -- 9.3.3 Ventricular and Supraventricular Tachycardias -- 9.3.4 Atrial Fibrillation -- 9.3.5 Atrial Flutter 9.4 Music Generation from Abnormal Heartbeats -- 9.4.1 A Retrieval Task -- 9.4.2 A Matter of Transformation -- 9.5 Conclusions and Discussion -- 10 Cognitive Musicology and Artificial Intelligence: Harmonic Analysis, Learning, and Generation -- 10.1 Introduction -- 10.2 Classical Artificial Intelligence Versus Deep Learning -- 10.3 Melodic Harmonization: Symbolic and Subsymbolic Models -- 10.4 Inventing New Concepts: Conceptual Blending in Harmony -- 10.5 Conclusions -- References -- 11 On Modelling Harmony with Constraint Programming for Algorithmic Composition Including a Model of Schoenberg's Theory of Harmony -- 11.1 Introduction -- 11.2 Application Examples -- 11.2.1 Automatic Melody Harmonisation -- 11.2.2 Modelling Schoenberg's Theory of Harmony -- 11.2.3 A Compositional Application in Extended Tonality -- 11.3 Overview: Constraint Programming for Modelling Harmony -- 11.3.1 Why Constraint Programming for Music Composition? -- 11.3.2 What Is Constraint Programming? -- 11.3.3 Music Constraint Systems for Algorithmic Composition -- 11.3.4 Harmony Modelling -- 11.3.5 Constraint-Based Harmony Systems -- 11.4 Case Study: A Constraint-Based Harmony Framework -- 11.4.1 Declaration of Chord and Scale Types -- 11.4.2 Temporal Music Representation -- 11.4.3 Chords and Scales -- 11.4.4 Notes with Analytical Information -- 11.4.5 Degrees, Accidentals and Enharmonic Spelling -- 11.4.6 Efficient Search with Constraint Propagation -- 11.4.7 Implementation -- 11.5 An Example: Modelling Schoenberg's Theory of Harmony -- 11.5.1 Score Topology -- 11.5.2 Pitch Resolution -- 11.5.3 Chord Types -- 11.5.4 Part Writing Rules -- 11.5.5 Simplified Root Progression Directions: Harmonic Band -- 11.5.6 Chord Inversions -- 11.5.7 Refined Root Progression Rules -- 11.5.8 Cadences -- 11.5.9 Dissonance Treatment -- 11.5.10 Modulation -- 11.6 Discussion 11.6.1 Comparison with Previous Systems -- 11.6.2 Limitations of the Framework -- 11.6.3 Completeness of Schoenberg Model -- 11.7 Future Research -- 11.7.1 Supporting Musical Form with Harmony -- 11.7.2 Combining Rule-Based Composition with Machine Learning -- 11.8 Summary -- 12 Constraint-Solving Systems in Music Creation -- 12.1 Introduction -- 12.2 Early Rule Formalizations for Computer-Generated Music -- 12.3 Improving Your Chances -- 12.4 Making Room for Exceptions -- 12.5 The Musical Challenge -- 12.6 Opening up for Creativity -- 12.7 The Need for Higher Efficiency -- 12.8 OMRC - greaterthan PWMC - greaterthan ClusterEngine -- 12.8.1 Musical Potential -- 12.8.2 Challenging Order -- 12.8.3 An Efficient User Interface -- 12.9 Future Developments and Final Remarks -- References -- 13 AI Music Mixing Systems -- 13.1 Introduction -- 13.2 Decision-Making Process -- 13.2.1 Knowledge Encoding -- 13.2.2 Expert Systems -- 13.2.3 Data Driven -- 13.2.4 Decision-Making Summary -- 13.3 Audio Manipulation -- 13.3.1 Adaptive Audio Effects -- 13.3.2 Direct Transformation -- 13.3.3 Audio Manipulation Summary -- 13.4 Human-Computer Interaction -- 13.4.1 Automatic -- 13.4.2 Independent -- 13.4.3 Recommendation -- 13.4.4 Discovery -- 13.4.5 Control-Level Summary -- 13.5 Further Design Considerations -- 13.5.1 Mixing by Sub-grouping -- 13.5.2 Intelligent Mixing Systems in Context -- 13.6 Discussion -- 13.7 The Future of Intelligent Mixing Systems -- 14 Machine Improvisation in Music: Information-Theoretical Approach -- 14.1 What Is Machine Improvisation -- 14.2 How It All Started: Motivation and Theoretical Setting -- 14.2.1 Part 1: Stochastic Modeling, Prediction, Compression, and Entropy -- 14.3 Generation of Music Sequences Using Lempel-Ziv (LZ) -- 14.3.1 Incremental Parsing -- 14.3.2 Generative Model Based on LZ. 14.4 Improved Suffix Search Using Factor Oracle Algorithm |
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| format | Electronic eBook |
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Creativity</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Cham</subfield><subfield code="b">Springer International Publishing AG</subfield><subfield code="c">2021</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (1007 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Foreword: From Audio Signals to Musical Meaning -- References -- Preface -- Contents -- Editor and Contributors -- 1 Sociocultural and Design 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3.3.2 Interactive Music Systems -- 3.3.3 Computational Aesthetic Evaluation -- 3.3.4 Human-Computer Co-exploration -- 3.4 Conceptual Considerations -- 3.4.1 The Computer as a Compositional Prosthesis -- 3.4.2 The Computer as a Virtual Player -- 3.4.3 Artificial Intelligence as a Secondary Agent -- 3.5 Limitations of Machine Learning -- 3.6 Composition and AI: The Road Ahead -- Acknowledgements -- References -- 4 Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry -- 4.1 Introduction -- 4.2 Combining Art, Science and Sound Research -- 4.2.1 Practice-Based Research and Objective Knowledge -- 4.2.2 Artistic Intervention in Scientific Research</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">4.3 Machine Learning as a Tool for Musical Performance -- 4.3.1 Corpus Nil -- 4.3.2 Scientific and Artistic Drives -- 4.3.3 Development and Observations -- 4.4 Artificial Intelligence as Actor in Performance -- 4.4.1 Humane Methods -- 4.4.2 Scientific and Artistic Drives -- 4.4.3 Development and Observations -- 4.5 Discussion -- 4.5.1 Artificial Intelligence and Music -- 4.5.2 From Machine Learning to Artificial Intelligence -- 4.5.3 Hybrid Methodology -- 5 Neuroscience of Musical Improvisation -- 5.1 Introduction -- 5.2 Cognitive Neuroscience of Music -- 5.3 Intrinsic Networks of the Brain -- 5.4 Temporally Precise Indices of Brain Activity in Music -- 5.5 Attention Toward Moments in Time -- 5.6 Prediction and Reward -- 5.7 Music and Language Learning -- 5.8 Conclusions: Creativity at Multiple Levels -- References -- 6 Discovering the Neuroanatomical Correlates of Music with Machine Learning -- 6.1 Introduction -- 6.2 Brain and Statistical Learning Machine -- 6.2.1 Prediction and Entropy Encoding -- 6.2.2 Learning -- 6.2.2.1 Timbre, Phoneme, and Pitch: Distributional Learning -- 6.2.2.2 Chunk and Word: Transitional Probability -- 6.2.2.3 Syntax and Grammar: Local Versus Non-local Dependencies -- 6.2.3 Memory -- 6.2.3.1 Semantic Versus Episodic -- 6.2.3.2 Short-Term Versus Long-Term -- 6.2.3.3 Consolidation -- 6.2.4 Action and Production -- 6.2.5 Social Communication -- 6.3 Computational Model -- 6.3.1 Mathematical Concepts of the Brain's Statistical Learning -- 6.3.2 Statistical Learning and the Neural Network -- 6.4 Neurobiological Model -- 6.4.1 Temporal Mechanism -- 6.4.2 Spatial Mechanism -- 6.4.2.1 Domain Generality Versus Domain Specificity -- 6.4.2.2 Probability Encoding -- 6.4.2.3 Uncertainty Encoding -- 6.4.2.4 Consolidation of Statistically Learned Knowledge -- 6.5 Future Direction: Creativity</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">6.5.1 Optimization for Creativity Rather than Efficiency -- 6.5.2 Cognitive Architectures -- 6.5.3 Neuroanatomical Correlates -- 6.5.3.1 Frontal Lobe -- 6.5.3.2 Cerebellum -- 6.5.3.3 Neural Network -- 6.6 Concluding Remarks -- Acknowledgements -- References -- 7 Music, Artificial Intelligence and Neuroscience -- 7.1 Introduction -- 7.2 Music -- 7.3 Artificial Intelligence -- 7.4 Neuroscience -- 7.5 Music and Neuroscience -- 7.6 Artificial Intelligence and Neuroscience -- 7.7 Music and Artificial Intelligence -- 7.8 Music, AI, and Neuroscience: A Test -- 7.9 Concluding Discussion -- References -- 8 Creative Music Neurotechnology -- 8.1 Introduction -- 8.2 Sound Synthesis with Real Neuronal Networks -- 8.3 Raster Plot: Making Music with Spiking Neurones -- 8.4 Symphony of Minds Listening: Listening to the Listening Mind -- 8.4.1 Brain Scanning and Analysis -- 8.4.2 The Compositional Process -- 8.4.3 The Musical Engine: MusEng -- 8.4.3.1 Learning Phase -- 8.4.3.2 Generative Phase -- 8.4.3.3 Transformative Phase -- Pitch Inversion Algorithm -- Pitch Scrambling Algorithm -- 8.5 Brain-Computer Music Interfacing -- 8.5.1 ICCMR's First SSVEP-Based BCMI System -- 8.5.2 Activating Memory and The Paramusical Ensemble -- 8.6 Concluding Discussion and Acknowledgements -- Acknowledgements -- Appendix: Two Pages of Raster Plot -- References -- 9 On Making Music with Heartbeats -- 9.1 Introduction -- 9.1.1 Why Cardiac Arrhythmias -- 9.1.2 Why Music Representation -- 9.1.3 Hearts Driving Music -- 9.2 Music Notation in Cardiac Auscultation -- 9.2.1 Venous Hum -- 9.2.2 Heart Murmurs -- 9.3 Music Notation of Cardiac Arrhythmias -- 9.3.1 Premature Ventricular and Atrial Contractions -- 9.3.2 A Theory of Beethoven and Arrhythmia -- 9.3.3 Ventricular and Supraventricular Tachycardias -- 9.3.4 Atrial Fibrillation -- 9.3.5 Atrial Flutter</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">9.4 Music Generation from Abnormal Heartbeats -- 9.4.1 A Retrieval Task -- 9.4.2 A Matter of Transformation -- 9.5 Conclusions and Discussion -- 10 Cognitive Musicology and Artificial Intelligence: Harmonic Analysis, Learning, and Generation -- 10.1 Introduction -- 10.2 Classical Artificial Intelligence Versus Deep Learning -- 10.3 Melodic Harmonization: Symbolic and Subsymbolic Models -- 10.4 Inventing New Concepts: Conceptual Blending in Harmony -- 10.5 Conclusions -- References -- 11 On Modelling Harmony with Constraint Programming for Algorithmic Composition Including a Model of Schoenberg's Theory of Harmony -- 11.1 Introduction -- 11.2 Application Examples -- 11.2.1 Automatic Melody Harmonisation -- 11.2.2 Modelling Schoenberg's Theory of Harmony -- 11.2.3 A Compositional Application in Extended Tonality -- 11.3 Overview: Constraint Programming for Modelling Harmony -- 11.3.1 Why Constraint Programming for Music Composition? -- 11.3.2 What Is Constraint Programming? -- 11.3.3 Music Constraint Systems for Algorithmic Composition -- 11.3.4 Harmony Modelling -- 11.3.5 Constraint-Based Harmony Systems -- 11.4 Case Study: A Constraint-Based Harmony Framework -- 11.4.1 Declaration of Chord and Scale Types -- 11.4.2 Temporal Music Representation -- 11.4.3 Chords and Scales -- 11.4.4 Notes with Analytical Information -- 11.4.5 Degrees, Accidentals and Enharmonic Spelling -- 11.4.6 Efficient Search with Constraint Propagation -- 11.4.7 Implementation -- 11.5 An Example: Modelling Schoenberg's Theory of Harmony -- 11.5.1 Score Topology -- 11.5.2 Pitch Resolution -- 11.5.3 Chord Types -- 11.5.4 Part Writing Rules -- 11.5.5 Simplified Root Progression Directions: Harmonic Band -- 11.5.6 Chord Inversions -- 11.5.7 Refined Root Progression Rules -- 11.5.8 Cadences -- 11.5.9 Dissonance Treatment -- 11.5.10 Modulation -- 11.6 Discussion</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11.6.1 Comparison with Previous Systems -- 11.6.2 Limitations of the Framework -- 11.6.3 Completeness of Schoenberg Model -- 11.7 Future Research -- 11.7.1 Supporting Musical Form with Harmony -- 11.7.2 Combining Rule-Based Composition with Machine Learning -- 11.8 Summary -- 12 Constraint-Solving Systems in Music Creation -- 12.1 Introduction -- 12.2 Early Rule Formalizations for Computer-Generated Music -- 12.3 Improving Your Chances -- 12.4 Making Room for Exceptions -- 12.5 The Musical Challenge -- 12.6 Opening up for Creativity -- 12.7 The Need for Higher Efficiency -- 12.8 OMRC - greaterthan PWMC - greaterthan ClusterEngine -- 12.8.1 Musical Potential -- 12.8.2 Challenging Order -- 12.8.3 An Efficient User Interface -- 12.9 Future Developments and Final Remarks -- References -- 13 AI Music Mixing Systems -- 13.1 Introduction -- 13.2 Decision-Making Process -- 13.2.1 Knowledge Encoding -- 13.2.2 Expert Systems -- 13.2.3 Data Driven -- 13.2.4 Decision-Making Summary -- 13.3 Audio Manipulation -- 13.3.1 Adaptive Audio Effects -- 13.3.2 Direct Transformation -- 13.3.3 Audio Manipulation Summary -- 13.4 Human-Computer Interaction -- 13.4.1 Automatic -- 13.4.2 Independent -- 13.4.3 Recommendation -- 13.4.4 Discovery -- 13.4.5 Control-Level Summary -- 13.5 Further Design Considerations -- 13.5.1 Mixing by Sub-grouping -- 13.5.2 Intelligent Mixing Systems in Context -- 13.6 Discussion -- 13.7 The Future of Intelligent Mixing Systems -- 14 Machine Improvisation in Music: Information-Theoretical Approach -- 14.1 What Is Machine Improvisation -- 14.2 How It All Started: Motivation and Theoretical Setting -- 14.2.1 Part 1: Stochastic Modeling, Prediction, Compression, and Entropy -- 14.3 Generation of Music Sequences Using Lempel-Ziv (LZ) -- 14.3.1 Incremental Parsing -- 14.3.2 Generative Model Based on LZ.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">14.4 Improved Suffix Search Using Factor Oracle Algorithm</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence-Musical applications</subfield></datafield><datafield tag="653" ind1=" " ind2="6"><subfield code="a">Electronic books</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Miranda, Eduardo Reck</subfield><subfield code="t">Handbook of Artificial 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| id | DE-604.BV048935494 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:58:11Z |
| indexdate | 2024-07-10T09:50:21Z |
| institution | BVB |
| isbn | 9783030721169 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034199360 |
| oclc_num | 1259627772 |
| open_access_boolean | |
| owner | DE-12 |
| owner_facet | DE-12 |
| physical | 1 Online-Ressource (1007 Seiten) |
| psigel | ZDB-1-PQM ZDB-30-PQE ZDB-1-PQM BSB_PDA_PQM |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| spelling | Miranda, Eduardo Reck Verfasser aut Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity Cham Springer International Publishing AG 2021 ©2021 1 Online-Ressource (1007 Seiten) txt rdacontent c rdamedia cr rdacarrier Intro -- Foreword: From Audio Signals to Musical Meaning -- References -- Preface -- Contents -- Editor and Contributors -- 1 Sociocultural and Design Perspectives on AI-Based Music Production: Why Do We Make Music and What Changes if AI Makes It for Us? -- 1.1 Introduction -- 1.2 The Philosophical Era -- 1.3 Creative Cognition and Lofty Versus Lowly Computational Creativity -- 1.4 The Design Turn -- 1.4.1 Design Evaluation -- 1.5 The Sociological View -- 1.5.1 Cluster Concepts and Emic Versus Etic Definitions -- 1.5.2 Social Perspectives on the Psychology of Creativity -- 1.5.3 Social Theories of Taste and Identity -- 1.5.4 Why Do We Make and Listen to Music? -- 1.6 Discussion -- 2 Human-Machine Simultaneity in the Compositional Process -- 2.1 Introduction -- 2.2 Machine as Projection Space -- 2.3 Temporal Interleaving -- 2.4 Work -- 2.5 Artistic Research -- 2.6 Suspension -- 3 Artificial Intelligence for Music Composition -- 3.1 Introduction -- 3.2 Artificial Intelligence and Distributed Human-Computer Co-creativity -- 3.3 Machine Learning: Applications in Music and Compositional Potential -- 3.3.1 Digital Musical Instruments -- 3.3.2 Interactive Music Systems -- 3.3.3 Computational Aesthetic Evaluation -- 3.3.4 Human-Computer Co-exploration -- 3.4 Conceptual Considerations -- 3.4.1 The Computer as a Compositional Prosthesis -- 3.4.2 The Computer as a Virtual Player -- 3.4.3 Artificial Intelligence as a Secondary Agent -- 3.5 Limitations of Machine Learning -- 3.6 Composition and AI: The Road Ahead -- Acknowledgements -- References -- 4 Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry -- 4.1 Introduction -- 4.2 Combining Art, Science and Sound Research -- 4.2.1 Practice-Based Research and Objective Knowledge -- 4.2.2 Artistic Intervention in Scientific Research 4.3 Machine Learning as a Tool for Musical Performance -- 4.3.1 Corpus Nil -- 4.3.2 Scientific and Artistic Drives -- 4.3.3 Development and Observations -- 4.4 Artificial Intelligence as Actor in Performance -- 4.4.1 Humane Methods -- 4.4.2 Scientific and Artistic Drives -- 4.4.3 Development and Observations -- 4.5 Discussion -- 4.5.1 Artificial Intelligence and Music -- 4.5.2 From Machine Learning to Artificial Intelligence -- 4.5.3 Hybrid Methodology -- 5 Neuroscience of Musical Improvisation -- 5.1 Introduction -- 5.2 Cognitive Neuroscience of Music -- 5.3 Intrinsic Networks of the Brain -- 5.4 Temporally Precise Indices of Brain Activity in Music -- 5.5 Attention Toward Moments in Time -- 5.6 Prediction and Reward -- 5.7 Music and Language Learning -- 5.8 Conclusions: Creativity at Multiple Levels -- References -- 6 Discovering the Neuroanatomical Correlates of Music with Machine Learning -- 6.1 Introduction -- 6.2 Brain and Statistical Learning Machine -- 6.2.1 Prediction and Entropy Encoding -- 6.2.2 Learning -- 6.2.2.1 Timbre, Phoneme, and Pitch: Distributional Learning -- 6.2.2.2 Chunk and Word: Transitional Probability -- 6.2.2.3 Syntax and Grammar: Local Versus Non-local Dependencies -- 6.2.3 Memory -- 6.2.3.1 Semantic Versus Episodic -- 6.2.3.2 Short-Term Versus Long-Term -- 6.2.3.3 Consolidation -- 6.2.4 Action and Production -- 6.2.5 Social Communication -- 6.3 Computational Model -- 6.3.1 Mathematical Concepts of the Brain's Statistical Learning -- 6.3.2 Statistical Learning and the Neural Network -- 6.4 Neurobiological Model -- 6.4.1 Temporal Mechanism -- 6.4.2 Spatial Mechanism -- 6.4.2.1 Domain Generality Versus Domain Specificity -- 6.4.2.2 Probability Encoding -- 6.4.2.3 Uncertainty Encoding -- 6.4.2.4 Consolidation of Statistically Learned Knowledge -- 6.5 Future Direction: Creativity 6.5.1 Optimization for Creativity Rather than Efficiency -- 6.5.2 Cognitive Architectures -- 6.5.3 Neuroanatomical Correlates -- 6.5.3.1 Frontal Lobe -- 6.5.3.2 Cerebellum -- 6.5.3.3 Neural Network -- 6.6 Concluding Remarks -- Acknowledgements -- References -- 7 Music, Artificial Intelligence and Neuroscience -- 7.1 Introduction -- 7.2 Music -- 7.3 Artificial Intelligence -- 7.4 Neuroscience -- 7.5 Music and Neuroscience -- 7.6 Artificial Intelligence and Neuroscience -- 7.7 Music and Artificial Intelligence -- 7.8 Music, AI, and Neuroscience: A Test -- 7.9 Concluding Discussion -- References -- 8 Creative Music Neurotechnology -- 8.1 Introduction -- 8.2 Sound Synthesis with Real Neuronal Networks -- 8.3 Raster Plot: Making Music with Spiking Neurones -- 8.4 Symphony of Minds Listening: Listening to the Listening Mind -- 8.4.1 Brain Scanning and Analysis -- 8.4.2 The Compositional Process -- 8.4.3 The Musical Engine: MusEng -- 8.4.3.1 Learning Phase -- 8.4.3.2 Generative Phase -- 8.4.3.3 Transformative Phase -- Pitch Inversion Algorithm -- Pitch Scrambling Algorithm -- 8.5 Brain-Computer Music Interfacing -- 8.5.1 ICCMR's First SSVEP-Based BCMI System -- 8.5.2 Activating Memory and The Paramusical Ensemble -- 8.6 Concluding Discussion and Acknowledgements -- Acknowledgements -- Appendix: Two Pages of Raster Plot -- References -- 9 On Making Music with Heartbeats -- 9.1 Introduction -- 9.1.1 Why Cardiac Arrhythmias -- 9.1.2 Why Music Representation -- 9.1.3 Hearts Driving Music -- 9.2 Music Notation in Cardiac Auscultation -- 9.2.1 Venous Hum -- 9.2.2 Heart Murmurs -- 9.3 Music Notation of Cardiac Arrhythmias -- 9.3.1 Premature Ventricular and Atrial Contractions -- 9.3.2 A Theory of Beethoven and Arrhythmia -- 9.3.3 Ventricular and Supraventricular Tachycardias -- 9.3.4 Atrial Fibrillation -- 9.3.5 Atrial Flutter 9.4 Music Generation from Abnormal Heartbeats -- 9.4.1 A Retrieval Task -- 9.4.2 A Matter of Transformation -- 9.5 Conclusions and Discussion -- 10 Cognitive Musicology and Artificial Intelligence: Harmonic Analysis, Learning, and Generation -- 10.1 Introduction -- 10.2 Classical Artificial Intelligence Versus Deep Learning -- 10.3 Melodic Harmonization: Symbolic and Subsymbolic Models -- 10.4 Inventing New Concepts: Conceptual Blending in Harmony -- 10.5 Conclusions -- References -- 11 On Modelling Harmony with Constraint Programming for Algorithmic Composition Including a Model of Schoenberg's Theory of Harmony -- 11.1 Introduction -- 11.2 Application Examples -- 11.2.1 Automatic Melody Harmonisation -- 11.2.2 Modelling Schoenberg's Theory of Harmony -- 11.2.3 A Compositional Application in Extended Tonality -- 11.3 Overview: Constraint Programming for Modelling Harmony -- 11.3.1 Why Constraint Programming for Music Composition? -- 11.3.2 What Is Constraint Programming? -- 11.3.3 Music Constraint Systems for Algorithmic Composition -- 11.3.4 Harmony Modelling -- 11.3.5 Constraint-Based Harmony Systems -- 11.4 Case Study: A Constraint-Based Harmony Framework -- 11.4.1 Declaration of Chord and Scale Types -- 11.4.2 Temporal Music Representation -- 11.4.3 Chords and Scales -- 11.4.4 Notes with Analytical Information -- 11.4.5 Degrees, Accidentals and Enharmonic Spelling -- 11.4.6 Efficient Search with Constraint Propagation -- 11.4.7 Implementation -- 11.5 An Example: Modelling Schoenberg's Theory of Harmony -- 11.5.1 Score Topology -- 11.5.2 Pitch Resolution -- 11.5.3 Chord Types -- 11.5.4 Part Writing Rules -- 11.5.5 Simplified Root Progression Directions: Harmonic Band -- 11.5.6 Chord Inversions -- 11.5.7 Refined Root Progression Rules -- 11.5.8 Cadences -- 11.5.9 Dissonance Treatment -- 11.5.10 Modulation -- 11.6 Discussion 11.6.1 Comparison with Previous Systems -- 11.6.2 Limitations of the Framework -- 11.6.3 Completeness of Schoenberg Model -- 11.7 Future Research -- 11.7.1 Supporting Musical Form with Harmony -- 11.7.2 Combining Rule-Based Composition with Machine Learning -- 11.8 Summary -- 12 Constraint-Solving Systems in Music Creation -- 12.1 Introduction -- 12.2 Early Rule Formalizations for Computer-Generated Music -- 12.3 Improving Your Chances -- 12.4 Making Room for Exceptions -- 12.5 The Musical Challenge -- 12.6 Opening up for Creativity -- 12.7 The Need for Higher Efficiency -- 12.8 OMRC - greaterthan PWMC - greaterthan ClusterEngine -- 12.8.1 Musical Potential -- 12.8.2 Challenging Order -- 12.8.3 An Efficient User Interface -- 12.9 Future Developments and Final Remarks -- References -- 13 AI Music Mixing Systems -- 13.1 Introduction -- 13.2 Decision-Making Process -- 13.2.1 Knowledge Encoding -- 13.2.2 Expert Systems -- 13.2.3 Data Driven -- 13.2.4 Decision-Making Summary -- 13.3 Audio Manipulation -- 13.3.1 Adaptive Audio Effects -- 13.3.2 Direct Transformation -- 13.3.3 Audio Manipulation Summary -- 13.4 Human-Computer Interaction -- 13.4.1 Automatic -- 13.4.2 Independent -- 13.4.3 Recommendation -- 13.4.4 Discovery -- 13.4.5 Control-Level Summary -- 13.5 Further Design Considerations -- 13.5.1 Mixing by Sub-grouping -- 13.5.2 Intelligent Mixing Systems in Context -- 13.6 Discussion -- 13.7 The Future of Intelligent Mixing Systems -- 14 Machine Improvisation in Music: Information-Theoretical Approach -- 14.1 What Is Machine Improvisation -- 14.2 How It All Started: Motivation and Theoretical Setting -- 14.2.1 Part 1: Stochastic Modeling, Prediction, Compression, and Entropy -- 14.3 Generation of Music Sequences Using Lempel-Ziv (LZ) -- 14.3.1 Incremental Parsing -- 14.3.2 Generative Model Based on LZ. 14.4 Improved Suffix Search Using Factor Oracle Algorithm Artificial intelligence-Musical applications Electronic books Erscheint auch als Druck-Ausgabe Miranda, Eduardo Reck Handbook of Artificial Intelligence for Music Cham : Springer International Publishing AG,c2021 9783030721152 |
| spellingShingle | Miranda, Eduardo Reck Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity Intro -- Foreword: From Audio Signals to Musical Meaning -- References -- Preface -- Contents -- Editor and Contributors -- 1 Sociocultural and Design Perspectives on AI-Based Music Production: Why Do We Make Music and What Changes if AI Makes It for Us? -- 1.1 Introduction -- 1.2 The Philosophical Era -- 1.3 Creative Cognition and Lofty Versus Lowly Computational Creativity -- 1.4 The Design Turn -- 1.4.1 Design Evaluation -- 1.5 The Sociological View -- 1.5.1 Cluster Concepts and Emic Versus Etic Definitions -- 1.5.2 Social Perspectives on the Psychology of Creativity -- 1.5.3 Social Theories of Taste and Identity -- 1.5.4 Why Do We Make and Listen to Music? -- 1.6 Discussion -- 2 Human-Machine Simultaneity in the Compositional Process -- 2.1 Introduction -- 2.2 Machine as Projection Space -- 2.3 Temporal Interleaving -- 2.4 Work -- 2.5 Artistic Research -- 2.6 Suspension -- 3 Artificial Intelligence for Music Composition -- 3.1 Introduction -- 3.2 Artificial Intelligence and Distributed Human-Computer Co-creativity -- 3.3 Machine Learning: Applications in Music and Compositional Potential -- 3.3.1 Digital Musical Instruments -- 3.3.2 Interactive Music Systems -- 3.3.3 Computational Aesthetic Evaluation -- 3.3.4 Human-Computer Co-exploration -- 3.4 Conceptual Considerations -- 3.4.1 The Computer as a Compositional Prosthesis -- 3.4.2 The Computer as a Virtual Player -- 3.4.3 Artificial Intelligence as a Secondary Agent -- 3.5 Limitations of Machine Learning -- 3.6 Composition and AI: The Road Ahead -- Acknowledgements -- References -- 4 Artificial Intelligence in Music and Performance: A Subjective Art-Research Inquiry -- 4.1 Introduction -- 4.2 Combining Art, Science and Sound Research -- 4.2.1 Practice-Based Research and Objective Knowledge -- 4.2.2 Artistic Intervention in Scientific Research 4.3 Machine Learning as a Tool for Musical Performance -- 4.3.1 Corpus Nil -- 4.3.2 Scientific and Artistic Drives -- 4.3.3 Development and Observations -- 4.4 Artificial Intelligence as Actor in Performance -- 4.4.1 Humane Methods -- 4.4.2 Scientific and Artistic Drives -- 4.4.3 Development and Observations -- 4.5 Discussion -- 4.5.1 Artificial Intelligence and Music -- 4.5.2 From Machine Learning to Artificial Intelligence -- 4.5.3 Hybrid Methodology -- 5 Neuroscience of Musical Improvisation -- 5.1 Introduction -- 5.2 Cognitive Neuroscience of Music -- 5.3 Intrinsic Networks of the Brain -- 5.4 Temporally Precise Indices of Brain Activity in Music -- 5.5 Attention Toward Moments in Time -- 5.6 Prediction and Reward -- 5.7 Music and Language Learning -- 5.8 Conclusions: Creativity at Multiple Levels -- References -- 6 Discovering the Neuroanatomical Correlates of Music with Machine Learning -- 6.1 Introduction -- 6.2 Brain and Statistical Learning Machine -- 6.2.1 Prediction and Entropy Encoding -- 6.2.2 Learning -- 6.2.2.1 Timbre, Phoneme, and Pitch: Distributional Learning -- 6.2.2.2 Chunk and Word: Transitional Probability -- 6.2.2.3 Syntax and Grammar: Local Versus Non-local Dependencies -- 6.2.3 Memory -- 6.2.3.1 Semantic Versus Episodic -- 6.2.3.2 Short-Term Versus Long-Term -- 6.2.3.3 Consolidation -- 6.2.4 Action and Production -- 6.2.5 Social Communication -- 6.3 Computational Model -- 6.3.1 Mathematical Concepts of the Brain's Statistical Learning -- 6.3.2 Statistical Learning and the Neural Network -- 6.4 Neurobiological Model -- 6.4.1 Temporal Mechanism -- 6.4.2 Spatial Mechanism -- 6.4.2.1 Domain Generality Versus Domain Specificity -- 6.4.2.2 Probability Encoding -- 6.4.2.3 Uncertainty Encoding -- 6.4.2.4 Consolidation of Statistically Learned Knowledge -- 6.5 Future Direction: Creativity 6.5.1 Optimization for Creativity Rather than Efficiency -- 6.5.2 Cognitive Architectures -- 6.5.3 Neuroanatomical Correlates -- 6.5.3.1 Frontal Lobe -- 6.5.3.2 Cerebellum -- 6.5.3.3 Neural Network -- 6.6 Concluding Remarks -- Acknowledgements -- References -- 7 Music, Artificial Intelligence and Neuroscience -- 7.1 Introduction -- 7.2 Music -- 7.3 Artificial Intelligence -- 7.4 Neuroscience -- 7.5 Music and Neuroscience -- 7.6 Artificial Intelligence and Neuroscience -- 7.7 Music and Artificial Intelligence -- 7.8 Music, AI, and Neuroscience: A Test -- 7.9 Concluding Discussion -- References -- 8 Creative Music Neurotechnology -- 8.1 Introduction -- 8.2 Sound Synthesis with Real Neuronal Networks -- 8.3 Raster Plot: Making Music with Spiking Neurones -- 8.4 Symphony of Minds Listening: Listening to the Listening Mind -- 8.4.1 Brain Scanning and Analysis -- 8.4.2 The Compositional Process -- 8.4.3 The Musical Engine: MusEng -- 8.4.3.1 Learning Phase -- 8.4.3.2 Generative Phase -- 8.4.3.3 Transformative Phase -- Pitch Inversion Algorithm -- Pitch Scrambling Algorithm -- 8.5 Brain-Computer Music Interfacing -- 8.5.1 ICCMR's First SSVEP-Based BCMI System -- 8.5.2 Activating Memory and The Paramusical Ensemble -- 8.6 Concluding Discussion and Acknowledgements -- Acknowledgements -- Appendix: Two Pages of Raster Plot -- References -- 9 On Making Music with Heartbeats -- 9.1 Introduction -- 9.1.1 Why Cardiac Arrhythmias -- 9.1.2 Why Music Representation -- 9.1.3 Hearts Driving Music -- 9.2 Music Notation in Cardiac Auscultation -- 9.2.1 Venous Hum -- 9.2.2 Heart Murmurs -- 9.3 Music Notation of Cardiac Arrhythmias -- 9.3.1 Premature Ventricular and Atrial Contractions -- 9.3.2 A Theory of Beethoven and Arrhythmia -- 9.3.3 Ventricular and Supraventricular Tachycardias -- 9.3.4 Atrial Fibrillation -- 9.3.5 Atrial Flutter 9.4 Music Generation from Abnormal Heartbeats -- 9.4.1 A Retrieval Task -- 9.4.2 A Matter of Transformation -- 9.5 Conclusions and Discussion -- 10 Cognitive Musicology and Artificial Intelligence: Harmonic Analysis, Learning, and Generation -- 10.1 Introduction -- 10.2 Classical Artificial Intelligence Versus Deep Learning -- 10.3 Melodic Harmonization: Symbolic and Subsymbolic Models -- 10.4 Inventing New Concepts: Conceptual Blending in Harmony -- 10.5 Conclusions -- References -- 11 On Modelling Harmony with Constraint Programming for Algorithmic Composition Including a Model of Schoenberg's Theory of Harmony -- 11.1 Introduction -- 11.2 Application Examples -- 11.2.1 Automatic Melody Harmonisation -- 11.2.2 Modelling Schoenberg's Theory of Harmony -- 11.2.3 A Compositional Application in Extended Tonality -- 11.3 Overview: Constraint Programming for Modelling Harmony -- 11.3.1 Why Constraint Programming for Music Composition? -- 11.3.2 What Is Constraint Programming? -- 11.3.3 Music Constraint Systems for Algorithmic Composition -- 11.3.4 Harmony Modelling -- 11.3.5 Constraint-Based Harmony Systems -- 11.4 Case Study: A Constraint-Based Harmony Framework -- 11.4.1 Declaration of Chord and Scale Types -- 11.4.2 Temporal Music Representation -- 11.4.3 Chords and Scales -- 11.4.4 Notes with Analytical Information -- 11.4.5 Degrees, Accidentals and Enharmonic Spelling -- 11.4.6 Efficient Search with Constraint Propagation -- 11.4.7 Implementation -- 11.5 An Example: Modelling Schoenberg's Theory of Harmony -- 11.5.1 Score Topology -- 11.5.2 Pitch Resolution -- 11.5.3 Chord Types -- 11.5.4 Part Writing Rules -- 11.5.5 Simplified Root Progression Directions: Harmonic Band -- 11.5.6 Chord Inversions -- 11.5.7 Refined Root Progression Rules -- 11.5.8 Cadences -- 11.5.9 Dissonance Treatment -- 11.5.10 Modulation -- 11.6 Discussion 11.6.1 Comparison with Previous Systems -- 11.6.2 Limitations of the Framework -- 11.6.3 Completeness of Schoenberg Model -- 11.7 Future Research -- 11.7.1 Supporting Musical Form with Harmony -- 11.7.2 Combining Rule-Based Composition with Machine Learning -- 11.8 Summary -- 12 Constraint-Solving Systems in Music Creation -- 12.1 Introduction -- 12.2 Early Rule Formalizations for Computer-Generated Music -- 12.3 Improving Your Chances -- 12.4 Making Room for Exceptions -- 12.5 The Musical Challenge -- 12.6 Opening up for Creativity -- 12.7 The Need for Higher Efficiency -- 12.8 OMRC - greaterthan PWMC - greaterthan ClusterEngine -- 12.8.1 Musical Potential -- 12.8.2 Challenging Order -- 12.8.3 An Efficient User Interface -- 12.9 Future Developments and Final Remarks -- References -- 13 AI Music Mixing Systems -- 13.1 Introduction -- 13.2 Decision-Making Process -- 13.2.1 Knowledge Encoding -- 13.2.2 Expert Systems -- 13.2.3 Data Driven -- 13.2.4 Decision-Making Summary -- 13.3 Audio Manipulation -- 13.3.1 Adaptive Audio Effects -- 13.3.2 Direct Transformation -- 13.3.3 Audio Manipulation Summary -- 13.4 Human-Computer Interaction -- 13.4.1 Automatic -- 13.4.2 Independent -- 13.4.3 Recommendation -- 13.4.4 Discovery -- 13.4.5 Control-Level Summary -- 13.5 Further Design Considerations -- 13.5.1 Mixing by Sub-grouping -- 13.5.2 Intelligent Mixing Systems in Context -- 13.6 Discussion -- 13.7 The Future of Intelligent Mixing Systems -- 14 Machine Improvisation in Music: Information-Theoretical Approach -- 14.1 What Is Machine Improvisation -- 14.2 How It All Started: Motivation and Theoretical Setting -- 14.2.1 Part 1: Stochastic Modeling, Prediction, Compression, and Entropy -- 14.3 Generation of Music Sequences Using Lempel-Ziv (LZ) -- 14.3.1 Incremental Parsing -- 14.3.2 Generative Model Based on LZ. 14.4 Improved Suffix Search Using Factor Oracle Algorithm Artificial intelligence-Musical applications |
| title | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_auth | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_exact_search | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_exact_search_txtP | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_full | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_fullStr | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_full_unstemmed | Handbook of Artificial Intelligence for Music Foundations, Advanced Approaches, and Developments for Creativity |
| title_short | Handbook of Artificial Intelligence for Music |
| title_sort | handbook of artificial intelligence for music foundations advanced approaches and developments for creativity |
| title_sub | Foundations, Advanced Approaches, and Developments for Creativity |
| topic | Artificial intelligence-Musical applications |
| topic_facet | Artificial intelligence-Musical applications |
| work_keys_str_mv | AT mirandaeduardoreck handbookofartificialintelligenceformusicfoundationsadvancedapproachesanddevelopmentsforcreativity |