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,$d[2021] 210 4$dİ2021 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 03874nam 22006854a 450 001 9910780035303321 005 20200520144314.0 010 $a1-280-20032-4 010 $a9786610200320 010 $a0-306-47545-6 024 7 $a10.1007/b117767 035 $a(CKB)111056486606598 035 $a(EBL)3035619 035 $a(SSID)ssj0000139434 035 $a(PQKBManifestationID)11143739 035 $a(PQKBTitleCode)TC0000139434 035 $a(PQKBWorkID)10007437 035 $a(PQKB)11343086 035 $a(DE-He213)978-0-306-47545-0 035 $a(MiAaPQ)EBC3035619 035 $a(Au-PeEL)EBL3035619 035 $a(CaPaEBR)ebr10052600 035 $a(CaONFJC)MIL20032 035 $a(OCoLC)70749645 035 $a(PPN)237935988 035 $a(EXLCZ)99111056486606598 100 $a20011101d2001 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aDirect conversion receivers in wide-band systems$b[electronic resource] /$fby Aarno Par?ssinen 205 $a1st ed. 2001. 210 $aBoston $cKluwer Academic Publishers$dc2001 215 $a1 online resource (251 p.) 225 1 $aThe Kluwer international series in engineering and computer science ;$vSECS 655 300 $aDescription based upon print version of record. 311 $a0-7923-7607-2 320 $aIncludes bibliographical references. 327 $aSystem Requirements for Radio Receivers in Wireless Communications -- Receiver Architectures -- Direct Conversion Receivers -- Circuit Implementations. 330 $aThis book is based on my doctoral thesis at the Helsinki University of Technology. Several different projects during five years guided me from the basics of the RF IC design to the implementations of highly integrated radio receiver chips. Sharing time and effort between IC and system issues is not always straightforward. I have been lucky to follow both topics and share experiences with diligent and enthusiastic people having different specialities. As a result, this book will cover a wide range of different topics needed in the design of highly integrated radio receivers. Experiences from the first receiver prototypes for the third generation cellular systems form the basis of this book. Most of the issues are directly related to the early proposals of European and Japanese standardization organizations. For example, the chip rate was originally set to 4. 096 Mcps in a wide-band CDMA channel. I have kept that number in the book in most of the examples although it has been later changed to 3. 84 Mcps. I hope that the readers will accept that and the possible other incompabilities to the latest specifications. At least in the research phase the changes even in the most essential requirements are definitely not a rare incident and IC designers should be able to react and modify their designs as soon as they can. 410 0$aKluwer international series in engineering and computer science ;$vSECS 655. 606 $aIntegrated circuits$xDesign and construction 606 $aBroadband communication systems$xEquipment and supplies 606 $aRadio circuits$xDesign and construction 606 $aRadio$xTransmitter-receivers$xEquipment and supplies$xDesign and construction 615 0$aIntegrated circuits$xDesign and construction. 615 0$aBroadband communication systems$xEquipment and supplies. 615 0$aRadio circuits$xDesign and construction. 615 0$aRadio$xTransmitter-receivers$xEquipment and supplies$xDesign and construction. 676 $a621.384/18 700 $aPa?rssinen$b Aarno$01531173 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910780035303321 996 $aDirect conversion receivers in wide-band systems$93776596 997 $aUNINA LEADER 04668nam 2200745 450 001 9910821804103321 005 20210422205743.0 010 $a1-61451-519-0 010 $a1-61451-961-7 024 7 $a10.1515/9781614515197 035 $a(CKB)3360000000515149 035 $a(EBL)1377166 035 $a(SSID)ssj0001401440 035 $a(PQKBManifestationID)11926794 035 $a(PQKBTitleCode)TC0001401440 035 $a(PQKBWorkID)11351066 035 $a(PQKB)11583152 035 $a(MiAaPQ)EBC1377166 035 $a(DE-B1597)245947 035 $a(OCoLC)1013954514 035 $a(OCoLC)951149790 035 $a(DE-B1597)9781614515197 035 $a(Au-PeEL)EBL1377166 035 $a(CaPaEBR)ebr11006119 035 $a(CaONFJC)MIL807729 035 $a(OCoLC)897445925 035 $a(PPN)185926800 035 $a(EXLCZ)993360000000515149 100 $a20140731h20142014 uy| 0 101 0 $aeng 135 $aur|nu---|u||u 181 $ctxt 182 $cc 183 $acr 200 04$aThe author in Middle Byzantine literature $emodes, functions, and identities /$fedited by Aglae Pizzone 210 1$aBerlin ;$aBoston :$cDe Gruyter,$d[2014] 210 4$dİ2014 215 $a1 online resource (368 p.) 225 1 $aByzantinisches Archiv ;$vvolume 28 300 $aDescription based upon print version of record. 311 $a1-61451-711-8 320 $aIncludes bibliographical references and index. 327 $tFront matter --$tContents --$tNote on citation and transliteration --$tNotes on contributors --$tList of abbreviations --$tIntroduction --$tThe Author in Middle Byzantine Literature: A View from Within /$rPizzone, Aglae --$tFirst Part: Modes --$tVoice, Signature, Mask: The Byzantine Author /$rPapaioannou, Stratis --$tThe Ethics of Authorship: Some Tensions in the 11th Century /$rBernard, Floris --$tQuestions of Authorship and Genre in Chronicles of the Middle Byzantine Period: The Case of Michael Psellos' Historia Syntomos /$rTocci, Raimondo --$tHis, and Not His: The Poems of the Late Gregory the Monk /$rLauxtermann, Marc D. --$tAuthorship and Authority in the Book of the Philosopher Syntipas /$rToth, Ida --$tSecond Part: Functions --$tAuthorial Voice and Self-Presentation in a 9th-Century Hymn on the Prodigal Son /$rKrueger, Derek --$tAristocracy and Literary Production in the 10th Century /$rAndriollo, Luisa --$tThe Anonymous Poets of the Anthologia Marciana: Questions of Collection and Authorship /$rSpingou, Foteini --$t"Perhaps the Scholiast Was also a Drudge." Authorial Practices in Three Middle Byzantine Sub-Literary Writings /$rKenens, Ulrike --$tIn Search of the Monastic Author. Story-Telling, Anonymity and Innovation in the 12th Century /$rMullett, Margaret --$tThird Part: Identities --$tEmmanuel C. Bourbouhakis The End of ?????????. Authorial Identity and Authorial Intention in Michael Ch?niat?s' ???? ???? ??????????? ?? ????????????? /$rBourbouhakis, Emmanuel C. --$tAnonymity, Dispossession and Reappropriation in the Prolog of Nik?phoros Basilak?s /$rPizzone, Aglae --$tAuthorship and Gender (and) Identity. Women's Writing in the Middle Byzantine Period /$rRiehle, Alexander --$tThe Authorial Voice of Anna Komn?n? /$rNeville, Leonora --$tAfterword --$tA Perspective from the Far (Medieval) West on Byzantine Theories of Authorship /$rJohnson, Ian --$tBibliography --$tGeneral Index --$tIndex of authors and texts 330 $aAuthor and authorship have become increasingly important concepts in Byzantine literary studies. This volume provides the first comprehensive survey on strategies of authorship in Middle Byzantine literature and investigates the interaction between self-presentation and cultural production in a wide array of genres, providing new insights into how Byzantine intellectuals conceived of their own work and pursuits. 410 0$aByzantinisches Archiv ;$vBd. 28. 606 $aByzantine literature$xHistory and criticism 606 $aAuthorship 606 $aAuthors, Byzantine 607 $aByzantine Empire$xIntellectual life 610 $aByzantine literature. 610 $aMedieval literature. 610 $aauthorship studies. 615 0$aByzantine literature$xHistory and criticism. 615 0$aAuthorship. 615 0$aAuthors, Byzantine. 676 $a880.9/002 686 $aNH 9340$2rvk 702 $aPizzone$b Aglae M. V.$f1976- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910821804103321 996 $aThe author in Middle Byzantine literature$94010817 997 $aUNINA