LEADER 04148oam 2200733I 450 001 9910453155603321 005 20200520144314.0 010 $a1-136-29169-5 010 $a1-280-87322-1 010 $a9786613714534 010 $a1-136-29170-9 010 $a0-203-11464-7 024 7 $a10.4324/9780203114643 035 $a(CKB)2550000000104812 035 $a(EBL)981715 035 $a(OCoLC)804665798 035 $a(SSID)ssj0000741017 035 $a(PQKBManifestationID)12286851 035 $a(PQKBTitleCode)TC0000741017 035 $a(PQKBWorkID)10720632 035 $a(PQKB)11630697 035 $a(MiAaPQ)EBC981715 035 $a(Au-PeEL)EBL981715 035 $a(CaPaEBR)ebr10578123 035 $a(CaONFJC)MIL371453 035 $a(OCoLC)801557650 035 $a(EXLCZ)992550000000104812 100 $a20180706d2013 uy 0 101 0 $aeng 135 $aurgn|---muuuu 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aGame sense $epedagogy for performance, participation and enjoyment /$fRichard Light 210 1$aMilton Park, Abingdon, Oxon ;$aNew York :$cRoutledge,$d2013. 215 $a1 online resource (xv, 240 pages) $cillustrations 225 1 $aRoutledge studies in physical education and youth sport 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a0-415-53288-4 311 $a0-415-53287-6 320 $aIncludes bibliographical references and index. 327 $arizing learning in and through game sense -- 4. Game sense for physical education and sport coaching -- 5. Game sense pedagogy -- 6. Assessing knowledge-in-action in team games -- Pt. II. 7. Touch rugby -- 8. Oztag -- 9. Australian football -- 10. Soccer -- 11. Field hockey -- 12. Basketball -- 13. Netball -- 14. Cricket (kanga) -- 15. Softball -- 16. Ultimate frisbee -- 17. Volleyball. 330 $a"Game Sense is an exciting and innovative approach to coaching and physical education that places the game at the heart of the session. It encourages the player to develop skills in a realistic context, to become more tactically aware, to make better decisions, and to have more fun. Game Sense is a comprehensive, research-informed introduction to the Game Sense approach that defines and explores key concepts and essential pedagogical theory, and that offers an extensive series of practical examples and plans for using Game Sense in real teaching and coaching situations.The first section of the book helps the reader to understand how learning occurs and how this informs player-centred pedagogy, and explains the relationship between Game Sense and other approaches to Teaching Games for Understanding. The second section of the book demonstrates how the theory can be applied in practice, providing a detailed, step-by-step guide to using Game Sense in eleven sports, including soccer, basketball, field hockey and softball. No other book explores the Game Sense approach in such depth, or combines both the theory and innovative practical techniques. Game Sense is invaluable reading for all students of physical education or sports coaching, any in-service physical education teacher, or any sports coach working with children or young people. "--$cProvided by publisher. 410 0$aRoutledge studies in physical education and youth sport. 606 $aPhysical education and training$xStudy and teaching 606 $aSports$xStudy and teaching 606 $aSports for children$xStudy and teaching 606 $aCoaching (Athletics) 606 $aSports for children$xCoaching 608 $aElectronic books. 615 0$aPhysical education and training$xStudy and teaching. 615 0$aSports$xStudy and teaching. 615 0$aSports for children$xStudy and teaching. 615 0$aCoaching (Athletics) 615 0$aSports for children$xCoaching. 676 $a613.707 700 $aLight$b Richard$g(Richard Lawrence),$0849669 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910453155603321 996 $aGame sense$91897324 997 $aUNINA LEADER 10620nam 2200469 450 001 9910488719403321 005 20220327094514.0 010 $a3-030-72116-7 035 $a(CKB)4100000011979276 035 $a(MiAaPQ)EBC6676030 035 $a(Au-PeEL)EBL6676030 035 $a(OCoLC)1259627772 035 $a(PPN)260302198 035 $a(EXLCZ)994100000011979276 100 $a20220327d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aHandbook of artificial intelligence for music $efoundations, advanced approaches, and developments for creativity /$fEduardo Reck Miranda, editor 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$d©2021 215 $a1 online resource (1007 pages) 311 $a3-030-72115-9 327 $aIntro -- 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. 327 $a4.3 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. 327 $a6.5.1 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. 327 $a9.4 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. 327 $a11.6.1 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. 327 $a14.4 Improved Suffix Search Using Factor Oracle Algorithm. 606 $aArtificial intelligence$xMusical applications 615 0$aArtificial intelligence$xMusical applications. 676 $a006.45 702 $aMiranda$b Eduardo Reck$f1963- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910488719403321 996 $aHandbook of artificial intelligence for music$92814883 997 $aUNINA LEADER 01658nam0-22003971i-450 001 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