Handbook of Artificial Intelligence for Music: Foundations, Advanced Approaches, and Developments for Creativity

Description

This book presents comprehensive coverage of the latest advances in research into enabling machines to listen to and compose new music. It includes chapters introducing what we know about human musical intelligence and on how this knowledge can be simulated with AI. The development of interactive musical robots and emerging new approaches to AI-based musical creativity are also introduced, including brain–computer music interfaces, bio-processors and quantum computing. Artificial Intelligence (AI) technology permeates the music industry, from management systems for recording studios to recommendation systems for online commercialization of music through the Internet. Yet whereas AI for online music distribution is well advanced, this book focuses on a largely unexplored application:  AI for creating the actual musical content. 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 14.5 Lossless Versus Lossy Compression Methods for Machine Improvisation 14.6 Variable Markov Oracle 14.7 Query-Based Improvisation Algorithm 14.7.1 Query-Matching Algorithm 14.8 Part 2: Variational Encoding, Free Energy, and Rate Distortion 14.8.1 Variational Free Energy 14.8.2 Rate Distortion and Human Cognition 14.9 VAE Latent Information Bounds 14.10 Deep Music Information Dynamics 14.10.1 Representation–Prediction Rate Distortion 14.11 Relation to VMO Analysis 14.11.1 Controlling Information Rate Between Encoder and Decoder 14.12 Experimental Results 14.12.1 Experimental Results 14.13 Summary and Discussion 15 Structure, Abstraction and Reference in Artificial Musical Intelligence 15.1 Introduction 15.2 The Nature of Music 15.3 Hierarchy in Music Representation 15.4 Abstraction in Music Representation 15.5 Reference in Music Representation 15.6 Synthesis 16 Folk the Algorithms: (Mis)Applying Artificial Intelligence to Folk Music 16.1 Introduction 16.2 Music Artificial Intelligence and Its Application to Folk Music 16.2.1 1950s–60s 16.2.2 1970s–90s 16.2.3 2000s–10s 16.3 Modeling Folk Music Transcriptions with Long Short-Term Memory Networks 16.3.1 Long Short-Term Memory Networks 16.3.2 folk-rnn (v2) 16.3.3 folk-rnn (v3) 16.3.4 folk-rnn (vBeamSearch) 16.3.5 folk-rnn (vScandinavian) 16.4 Evaluation 16.4.1 Evaluation by Parameter Analysis 16.4.2 Evaluation by Co-creation 16.4.3 Evaluation by Cherry Picking: ``Let's Have Another Gan Ainm'' 16.5 Ethical Considerations 16.6 Conclusion 17 Automatic Music Composition with Evolutionary Algorithms: Digging into the Roots of Biological Creativity 17.1 Introduction 17.2 Lindenmayer Systems 17.3 Evolutionary Algorithms 17.3.1 Optimization Problems 17.3.2 Evolutionary Algorithms 17.3.3 Indirect Encoding 17.3.4 Evolving L-Systems 17.4 Melomics 17.4.1 Atonal Music 17.4.2 Examples of Atonal Music 17.4.3 Tonal Music 17.4.4 Example of Tonal Music: 0Music and the Web Repository 17.4.5 Example of Application: Music Therapy 17.4.6 Output Formats and Interoperability 17.5 A Soundtrack for Life 17.5.1 Is Artificial Music Actually Music? 17.5.2 Creation or Discovery? 17.5.3 Why Artificial Music? 17.6 Conclusions 18 Assisted Music Creation with Flow Machines: Towards New Categories of New 18.1 Background and Motivations 18.1.1 The Continuator 18.2 Markov Constraints: Main Scientific Results 18.2.1 The ``Markov + X'' Roadmap 18.2.2 Positional Constraints 18.2.3 Meter and All that Jazz 18.2.4 Sampling Methods 18.3 Beyond Markov Models 18.4 Flow Composer: The First AI-Assisted Lead Sheet Composition Tool 18.5 Significant Music Productions 18.6 Unfinished but Promising Projects 18.7 Impact and Followup 18.8 Lessons Learned 18.8.1 Better Model Does Not Imply Better Music 18.8.2 New Creative Acts 18.8.3 The Appropriation Effect 18.9 Towards New Categories of New 18.10 Conclusion 19 Performance Creativity in Computer Systems for Expressive Performance of Music 19.1 Introduction 19.1.1 Human Expressive Performance 19.1.2 Computer Expressive Performance 19.1.3 Performance Creativity 19.2 A Generic Framework for Previous Research in Computer Expressive Performance 19.2.1 Modules of Systems Reviewed 19.3 A Survey of Computer Systems for Expressive Music Performance 19.3.1 Non-Learning Systems 19.3.1.1 Director Musices 19.3.1.2 Hierarchical Parabola Model 19.3.1.3 Composer Pulse and Predictive Amplitude Shaping 19.3.1.4 Bach Fugue System 19.3.1.5 Trumpet Synthesis 19.3.1.6 Rubato 19.3.1.7 Pop-E 19.3.1.8 Hermode Tuning 19.3.1.9 Sibelius 19.3.1.10 Computational Music Emotion Rule System 19.3.2 Linear Regression 19.3.2.1 Music Interpretation System 19.3.2.2 CaRo 19.3.3 Artificial Neural Networks 19.3.3.1 Artificial Neural Network Piano System 19.3.3.2 Emotional Flute 19.3.3.3 User-Curated Piano 19.3.4 Case and Instance-Based Systems 19.3.4.1 SaxEx 19.3.4.2 Kagurame 19.3.4.3 Ha-Hi-Hun 19.3.4.4 PLCG System 19.3.4.5 Combined Phrase-Decomposition/PLCG 19.3.4.6 DISTALL System 19.3.5 Statistical Graphical Models 19.3.5.1 Music Plus One 19.3.5.2 ESP Piano System 19.3.6 Other Regression Methods 19.3.6.1 Drumming System 19.3.6.2 KCCA Piano System 19.3.7 Evolutionary Computation 19.3.7.1 Genetic Programming Jazz Sax 19.3.7.2 Sequential Covering Algorithm GAs 19.3.7.3 Jazz Guitar 19.3.7.4 Ossia 19.3.7.5 MASC 19.4 A Detailed Example: IMAP 19.4.1 Evolutionary Computation 19.4.2 IMAP Overview 19.4.2.1 Agent Evaluation Functions 19.4.2.2 Evaluation Equations 19.4.2.3 Agent Function Definitions 19.4.2.4 Agent Cycle 19.4.3 User-Generated Performances of IMAP 19.4.4 Experiments and Evaluation 19.4.4.1 Experiment 1: Can Agents Generate Performances Expressing Their “preference” Weights? 19.4.4.2 Experiment 2: Can One Control the Extent of the Performances’ Diversity? 19.4.4.3 Experiment 3: Controlling the Direction of the Performances’ Diversity 19.4.5 IMAP Summary 19.5 Concluding Remarks References 20 Imitative Computer-Aided Musical Orchestration with Biologically Inspired Algorithms 20.1 Introduction 20.1.1 Musical Orchestration 20.1.2 Musical Timbre 20.1.3 Musical Orchestration with the Aid of the Computer 20.2 State of the Art 20.2.1 Early Approaches 20.2.2 Generative Approaches 20.2.3 Machine Learning 20.3 Imitative Computer-Aided Musical Orchestration 20.3.1 Overview 20.3.2 Representation 20.3.3 Audio Descriptor Extraction 20.3.4 Pre-processing 20.3.5 Combination Functions 20.3.6 Distance Functions 20.3.7 Calculating the Fitness of Orchestrations 20.4 Computer-Aided Musical Orchestration with Bio-inspired Algorithms 20.4.1 Searching for Orchestrations for a Reference Sound 20.4.2 Finding Orchestrations for a Reference Sound 20.5 Discussion 20.5.1 Perceptual Considerations 20.5.2 Diversity of Orchestrations in CAMO-AIS 20.5.3 Dynamic Orchestrations with Orchids 20.5.4 Dynamic Orchestrations with Orchidea 20.6 Conclusions 21 Human-Centred Artificial Intelligence in Concatenative Sound Synthesis 21.1 Introduction 21.2 Sound Synthesis: A Brief Overview 21.3 How Can Concatenative Sound Synthesis Synthesize Sounds? 21.4 What Affects CSS Result? 21.5 At All Costs 21.6 Human-Centred Artificial Intelligence: That’s not What I Ordered 21.7 Is Similar, Interesting? 21.8 Where Are We Now? References 22 Deep Generative Models for Musical Audio Synthesis 22.1 Introduction 22.1.1 Overview 22.1.2 Generative Neural Networks 22.1.3 The Gift of Music: DNN-based Synthesizers 22.1.4 Only a Matter of Time: Real-Time Generation 22.1.5 The Answer Lies Within: Interfacing via Conditional Models 22.1.6 Along for the Ride: External Conditioning 22.1.7 Beneath the Surface: Latent Variable Models of Music 22.1.8 Build Me Up, Break Me Down: Audio Synthesis with GANs 22.1.9 A Change of Seasons: Music Translation 22.1.10 Discussion and Conclusion 23 Transfer Learning for Generalized Audio Signal Processing 23.1 Introduction 23.2 Feature Space Adaptation 23.2.1 Echo State Network 23.3 Use Cases 23.3.1 Affective Computing 23.3.2 Bird Species Identification 23.4 Conclusions and Future Directions 24 From Audio to Music Notation 24.1 Introduction 24.2 Problem Definition 24.3 Datasets and Evaluation Metrics 24.3.1 Datasets 24.3.2 Evaluation Metrics 24.4 State of the Art 24.4.1 Overview 24.4.2 Neural Networks for AMT 24.4.3 Multi-task Learning Methods 24.4.4 Music Language Models 24.4.5 Complete Transcription 24.5 Challenges 24.5.1 Datasets 24.5.2 Evaluation Metrics 24.5.3 Non-Western Music 24.5.4 Complete Transcription 24.5.5 Expressive Performance 24.5.6 Domain Adaptation 24.6 Conclusions 25 Automatic Transcription of Polyphonic Vocal Music 25.1 Introduction 25.2 Related Works 25.3 Polyphonic Vocal Music 25.3.1 Particular Characteristics of Vocal Sounds 25.3.2 Probabilistic Latent Component Analysis 25.4 PLCA Applied to Polyphonic Vocal Music 25.4.1 Dictionary Construction 25.4.2 Model 1: MSINGERS 25.4.3 Model 2: VOCAL4 25.4.4 Voice Assignment 25.5 Final Considerations 26 Graph-Based Representation, Analysis, and Interpretation of Popular Music Lyrics Using Semantic Embedding Features 26.1 Introduction 26.2 Key Concepts and Related Works 26.2.1 Deep Modeling of Lexical Meanings 26.2.2 Mapping Artificial Intelligence Research for Lyrics Studies 26.2.3 Believe in Data: Data-Driven Approaches for Lyrics Studies 26.2.4 Critical Re-definition from Empirical to Experimental 26.3 Semantic Word Embedding 26.4 Appending Relational Links 26.4.1 Appending Similarity Links 26.4.2 Appending Lyric Structural Links 26.4.3 Adjacency to the Key Analysis Concepts 26.5 Details of Feature Descriptors 26.5.1 Spatial Distribution Based Features 26.5.1.1 Centroid Location 26.5.1.2 Span Volume and Dispersion Between Semantic Word Embedding Dimensions 26.5.1.3 Maximum Semantic Span 26.5.1.4 Semantic Span Distribution 26.5.1.5 Semantic Span Imbalance Among Semantic Embedding Dimensions 26.5.1.6 Token Distributional Symmetry Based Descriptors 26.5.2 Temporal Structure-Based Features 26.5.2.1 Average Step Size 26.5.2.2 Variation Pattern of the Step Size 26.5.2.3 Average Adjacent Edge Angles 26.5.2.4 Variations of Adjacent Edge Angles 26.5.2.5 Mean of Adjacent Edge Angle Increment 26.5.2.6 Skip Length Descriptors 26.5.2.7 Symmetric Pattern of the Lyric Chain 26.5.3 Feature Descriptor for Graph Topology 26.5.3.1 Average Node Connectedness 26.5.3.2 Variation of Node Connectedness 26.5.3.3 Topological Balance of Node Connectedness 26.5.3.4 Page Rank Descriptor 26.5.3.5 Distribution of Edge Angles 26.5.3.6 Graph Symmetry Descriptors 26.5.3.7 Connection Topological Symmetry Based on Betweenness Centrality 26.5.4 Feature Descriptors on Graph Spectra and Other Analytical Graph Representations 26.5.4.1 Matrix Decomposition Based Descriptors 26.5.4.2 Root Mean Square of Spectra Span Volume 26.6 Empirical Studies 26.6.1 Studies on Distributional Patterns Over Genre Categories 26.6.2 Studies on Distributional Patterns from Different Time Period 26.7 Conclusions and Future Work References 27 Interactive Machine Learning of Musical Gesture 27.1 Introduction 27.1.1 Why Machine Learning Musical Gestures? Needs and Challenges 27.1.2 Chapter Overview 27.2 Machine-Sensing Gesture 27.2.1 Sensing Movement 27.2.2 Sensing the Body 27.3 Analysing Gesture 27.3.1 Motion Features 27.3.2 EMG Features 27.4 Machine Learning Techniques 27.4.1 Classification 27.4.2 Regression 27.4.3 Temporal Modelling 27.5 Sound Synthesis and Gesture Mapping 27.5.1 Granular Synthesis and Sound Tracing 27.5.2 Corpus-Based Synthesis and Feature Mapping 27.6 Reinforcement Learning 27.6.1 RL for Exploring Gesture-Sound Mappings: Assisted Interactive Machine Learning 27.6.2 AIML System Architecture 27.6.3 AIML Workflow 27.7 In Practice: IML Techniques in Musical Pieces 27.7.1 Wais (Tanaka) 27.7.2 11 Degrees of Dependence (Visi) 27.7.3 Delearning (Tanaka) 27.7.4 ``You Have a New Memory'' (Visi) 27.8 Conclusion 28 Human–Robot Musical Interaction 28.1 Introduction 28.2 Music, Interaction, and Robots 28.3 The Waseda Wind Robot Players 28.3.1 The Waseda Flutist WF 28.3.2 The Waseda Anthropomorphic Saxophonist WAS 28.4 Technical Musical Interaction 28.4.1 Asynchronous Verbal Technical Interaction 28.4.2 Synchronous Automatic Interaction 28.4.3 Interaction via Direct Signaling 28.4.4 Multimodal Dynamic Interaction 28.4.5 Technical Interaction in an Orchestra: Conducting Gestures 28.5 Creative Interaction 28.6 Emotional Interaction 28.7 Concluding Discussion References 29 Shimon Sings-Robotic Musicianship Finds Its Voice 29.1 Introduction—Robotic Musicianship at GTCMT 29.1.1 Platforms 29.1.2 Design Principles 29.2 ``Shimon Sings''—Motivation and Approach 29.3 Lyrics Generation 29.3.1 Implementation 29.3.2 Experiment 29.3.3 Results 29.4 Gesture Generation 29.4.1 Implementation 29.4.2 Experiment Methodology 29.4.3 Results 29.5 Discussion and Future Work 30 AI-Lectronica: Music AI in Clubs and Studio Production 30.1 The Artificial Intelligence Sonic Boom 30.2 Music Production Tools and AI 30.3 AIlgorAIve 30.4 A PersonAl PerspectAve: Shelly Knotts 30.4.1 CYOF 30.4.2 AlgoRIOTmic Grrrl! 30.4.3 Future Work 30.5 I PersonIl PerspectIve: Nick Collins 30.6 Conclusions References 31 Musicking with Algorithms: Thoughts on Artificial Intelligence, Creativity, and Agency 31.1 Introduction 31.1.1 AI and Art 31.1.2 Motivation 31.1.3 Properties of an Artist 31.1.4 Possibilities with AI in Art and Music 31.1.5 Art in AI 31.2 Agency 31.2.1 Influential Agency 31.2.2 Influential Agency of an Algorithm 31.2.3 Influence as Information 31.2.4 Influential Agency in a Typical AI Music Implementation 31.2.5 Influential Agency in an Actual Example: Ossia 31.2.6 Agency is Where in the Code? 31.3 Tools and Humans 31.3.1 Effort Versus Tool Complexity 31.3.2 Non-mediated Agency in Algorithms 31.4 Spectra of Agency 31.4.1 Spectrum of Tool Complexity 31.4.2 Spectrum of Agency 31.4.3 Spectrum of Generativity 31.5 Problems with Creative AI 31.5.1 The Inherent Non-creativity of Statistical Machine Learning 31.5.2 Opaqueness of AI-Generated Material 31.5.3 The Lack of a Model of the Outside World 31.6 Aesthetics 31.6.1 Autonomous Aesthetics and Agency 31.6.2 Characteristic Inability 31.6.3 Apparent Agency Attribution 31.6.4 Uncanny Valley 31.6.5 Authenticity 31.6.6 Human Measure 31.6.7 Cross-Species Art 31.6.8 The Role of Time—Learning as a Non-Real-time Process 31.6.9 Culture and Forgetting 31.7 Conclusions 31.7.1 Will AI Make Art-Making Easier? 31.7.2 The Road Ahead—Musicking with Algorithms References 32 cellF: Surrogate Musicianship as a Manifestation of In-Vitro Intelligence 32.1 Introduction 32.2 Origins and Development of the Work 32.3 Influences from the History of Modern Music 32.4 Influences from the Field of Robotic Musicianship 32.5 In-Vitro Intelligence 32.6 Surrogate Musicianship 32.7 Concluding Discussion References 33 On Growing Computers from Living Biological Cells 33.1 Introduction 33.2 Meet Physarum Polycephalum 33.3 Physarum Polycephalum Sonification 33.4 Developing the Biomemristor 33.4.1 Music Processing with Biomemristors 33.5 Performing Boolean Logic and Arithmetic Operations with the Biomemristor 33.5.1 Bio-Logic Operations 33.5.1.1 The OR Operator 33.5.1.2 The AND Operator 33.5.1.3 The NOT Operator 33.5.2 Towards Bio-Logic Electronic Circuits: Half ADDER 33.6 Concluding Remarks Acknowledgements References 34 Quantum Computer: Hello, Music! 34.1 Introduction 34.2 Historical Background 34.3 Algorithmic Computer Music 34.4 Quantum Computing Primer 34.5 Quantum Vocal Synthesizer 34.6 Quantum Walk Sequencer 34.7 Concluding Remarks Acknowledgements References