01740nam0 2200373 450 00001902620061111050102.090-6605-146-90567-757220060504d1994----km-y0itaa50------baengNL5. International symposium on plum and prune genetic, breeding and pomologyStuttgart - Hohenheim, 06-10 September 1993convener [and] editor W. HartmannSection for fruits, Working group plum and pruneWageningen (NL)International society for horticultural science1994296 p.24 cmActa horticulturae359Fifth international symposium on plum and prune genetic, breeding and pomology2001Acta horticulturae359Fifth international symposium on plum and prune genetic, breeding and pomologyOrticolturaCongressi635(21. ed.)OrticolturaHartmann,W.International symposium on plum and prune genetic, breeding and pomology<5.; 1993; Stuttgart - Hohenheim>442881International society for horticultural scienceSection for fruitsWorking group plum and pruneITUniversità della Basilicata - B.I.A.RICAunimarc0000190265. International symposium on plum and prune genetic, breeding and pomology83955UNIBASAGRARIASTD0440120060504BAS011224TTM3020061110BAS010450TTM3020061111BAS010501BAS01BAS01BOOKBASA2Polo Tecnico-ScientificoDIDDidatticaPTS77888A778882006050498Consultazione10620nam 2200469 450 991048871940332120220327094514.03-030-72116-7(CKB)4100000011979276(MiAaPQ)EBC6676030(Au-PeEL)EBL6676030(OCoLC)1259627772(PPN)260302198(EXLCZ)99410000001197927620220327d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierHandbook of artificial intelligence for music foundations, advanced approaches, and developments for creativity /Eduardo Reck Miranda, editorCham, Switzerland :Springer,[2021]©20211 online resource (1007 pages)3-030-72115-9 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 intelligenceMusical applicationsArtificial intelligenceMusical applications.006.45Miranda Eduardo Reck1963-MiAaPQMiAaPQMiAaPQBOOK9910488719403321Handbook of artificial intelligence for music2814883UNINA