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Artificial perception and music recognition / Andranick S. Tanguiane
| Artificial perception and music recognition / Andranick S. Tanguiane |
| Autore | Tanguiane, Andranick S. <1952- > |
| Pubbl/distr/stampa | Berlin [etc.], : Springer, c1993 |
| Descrizione fisica | XIV, 210 p. ; 24 cm |
| Disciplina |
006.4
006.45 |
| Collana | Lecture notes in computer science, . Lecture notes in artificial intelligence |
| ISBN |
0387573941
3540573941 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISANNIO-AQ10003816 |
Tanguiane, Andranick S. <1952- >
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| Berlin [etc.], : Springer, c1993 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Sannio | ||
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Fundamentals of music processing : using Python and Jupyter notebooks / / Meinard Müller
| Fundamentals of music processing : using Python and Jupyter notebooks / / Meinard Müller |
| Autore | Müller Meinard |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (523 pages) |
| Disciplina | 006.45 |
| Soggetto topico | Sound - Recording and reproducing - Digital techniques |
| ISBN | 3-030-69808-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910483504703321 |
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Müller Meinard
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Fundamentals of music processing : using Python and Jupyter notebooks / / Meinard Müller
| Fundamentals of music processing : using Python and Jupyter notebooks / / Meinard Müller |
| Autore | Müller Meinard |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (523 pages) |
| Disciplina | 006.45 |
| Soggetto topico | Sound - Recording and reproducing - Digital techniques |
| ISBN | 3-030-69808-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464521703316 |
|
Müller Meinard
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| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Fundamentals of Music Processing : Audio, Analysis, Algorithms, Applications / / by Meinard Müller
| Fundamentals of Music Processing : Audio, Analysis, Algorithms, Applications / / by Meinard Müller |
| Autore | Müller Meinard |
| Edizione | [1st ed. 2015.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 |
| Descrizione fisica | 1 online resource (XXIX, 487 p. 249 illus., 30 illus. in color.) |
| Disciplina | 006.45 |
| Soggetto topico |
Pattern recognition
Signal processing Image processing Speech processing systems Information storage and retrieval Fourier analysis Application software Music Pattern Recognition Signal, Image and Speech Processing Information Storage and Retrieval Fourier Analysis Computer Appl. in Arts and Humanities |
| ISBN | 3-319-21945-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1 Music Representations -- 2 Fourier Analysis of Signals -- 3 Music Synchronization -- 4 Music Structure Analysis -- 5 Chord Recognition -- 6 Tempo and Beat Tracking -- 7 Content-Based Audio Retrieval -- 8 Musically Informed Audio Decomposition. |
| Record Nr. | UNINA-9910299230403321 |
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Müller Meinard
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| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Handbook of artificial intelligence for music : foundations, advanced approaches, and developments for creativity / / Eduardo Reck Miranda, editor
| Handbook of artificial intelligence for music : foundations, advanced approaches, and developments for creativity / / Eduardo Reck Miranda, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1007 pages) |
| Disciplina | 006.45 |
| Soggetto topico | Artificial intelligence - Musical applications |
| ISBN | 3-030-72116-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNINA-9910488719403321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of artificial intelligence for music : foundations, advanced approaches, and developments for creativity / / Eduardo Reck Miranda, editor
| Handbook of artificial intelligence for music : foundations, advanced approaches, and developments for creativity / / Eduardo Reck Miranda, editor |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (1007 pages) |
| Disciplina | 006.45 |
| Soggetto topico | Artificial intelligence - Musical applications |
| ISBN | 3-030-72116-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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. |
| Record Nr. | UNISA-996464401303316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
An introduction to audio content analysis : music information retrieval tasks and applications / / Alexander Lerch
| An introduction to audio content analysis : music information retrieval tasks and applications / / Alexander Lerch |
| Autore | Lerch Alexander |
| Edizione | [Second edition.] |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley-IEEE Press, , 2023 |
| Descrizione fisica | 1 online resource (467 pages) |
| Disciplina | 006.45 |
| Soggetto topico |
Computational auditory scene analysis
Computer sound processing Content analysis (Communication) - Data processing |
| ISBN |
1-119-89098-5
1-119-89096-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910829842403321 |
|
Lerch Alexander
|
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| Hoboken, New Jersey : , : Wiley-IEEE Press, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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An introduction to audio content analysis : applications in signal processing and music informatics / / Alexander Lerch
| An introduction to audio content analysis : applications in signal processing and music informatics / / Alexander Lerch |
| Autore | Lerch Alexander |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , c2012 |
| Descrizione fisica | 1 online resource (272 p.) |
| Disciplina |
006.45
621.3822 |
| Soggetto topico |
Computer sound processing
Computational auditory scene analysis Content analysis (Communication) - Data processing |
| ISBN |
1-283-80405-0
1-118-39350-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Machine generated contents note: 1.1.Audio Content -- 1.2.A Generalized Audio Content Analysis System -- 2.1.Audio Signals -- 2.1.1.Periodic Signals -- 2.1.2.Random Signals -- 2.1.3.Sampling and Quantization -- 2.1.4.Statistical Signal Description -- 2.2.Signal Processing -- 2.2.1.Convolution -- 2.2.2.Block-Based Processing -- 2.2.3.Fourier Transform -- 2.2.4.Constant Q Transform -- 2.2.5.Auditory Filterbanks -- 2.2.6.Correlation Function -- 2.2.7.Linear Prediction -- 3.1.Audio Pre-Processing -- 3.1.1.Down-Mixing -- 3.1.2.DC Removal -- 3.1.3.Normalization -- 3.1.4.Down-Sampling -- 3.1.5.Other Pre-Processing Options -- 3.2.Statistical Properties -- 3.2.1.Arithmetic Mean -- 3.2.2.Geometric Mean -- 3.2.3.Harmonic Mean -- 3.2.4.Generalized Mean -- 3.2.5.Centroid -- 3.2.6.Variance and Standard Deviation -- 3.2.7.Skewness -- 3.2.8.Kurtosis -- 3.2.9.Generalized Central Moments -- 3.2.10.Quantiles and Quantile Ranges -- 3.3.Spectral Shape -- 3.3.1.Spectral Rolloff --
Contents note continued: 3.3.2.Spectral Flux -- 3.3.3.Spectral Centroid -- 3.3.4.Spectral Spread -- 3.3.5.Spectral Decrease -- 3.3.6.Spectral Slope -- 3.3.7.Mel Frequency Cepstral Coefficients -- 3.4.Signal Properties -- 3.4.1.Tonalness -- 3.4.2.Autocorrelation Coefficients -- 3.4.3.Zero Crossing Rate -- 3.5.Feature Post-Processing -- 3.5.1.Derived Features -- 3.5.2.Normalization and Mapping -- 3.5.3.Subfeatures -- 3.5.4.Feature Dimensionality Reduction -- 4.1.Human Perception of Intensity and Loudness -- 4.2.Representation of Dynamics in Music -- 4.3.Features -- 4.3.1.Root Mean Square -- 4.4.Peak Envelope -- 4.5.Psycho-Acoustic Loudness Features -- 4.5.1.EBU R128 -- 5.1.Human Perception of Pitch -- 5.1.1.Pitch Scales -- 5.1.2.Chroma Perception -- 5.2.Representation of Pitch in Music -- 5.2.1.Pitch Classes and Names -- 5.2.2.Intervals -- 5.2.3.Root Note, Mode, and Key -- 5.2.4.Chords and Harmony -- 5.2.5.The Frequency of Musical Pitch -- 5.3.Fundamental Frequency Detection -- Contents note continued: 5.3.1.Detection Accuracy -- 5.3.2.Pre-Processing -- 5.3.3.Monophonic Input Signals -- 5.3.4.Polyphonic Input Signals -- 5.4.Tuning Frequency Estimation -- 5.5.Key Detection -- 5.5.1.Pitch Chroma -- 5.5.2.Key Recognition -- 5.6.Chord Recognition -- 6.1.Human Perception of Temporal Events -- 6.1.1.Onsets -- 6.1.2.Tempo and Meter -- 6.1.3.Rhythm -- 6.1.4.Timing -- 6.2.Representation of Temporal Events in Music -- 6.2.1.Tempo and Time Signature -- 6.2.2.Note Value -- 6.3.Onset Detection -- 6.3.1.Novelty Function -- 6.3.2.Peak Picking -- 6.3.3.Evaluation -- 6.4.Beat Histogram -- 6.4.1.Beat Histogram Features -- 6.5.Detection of Tempo and Beat Phase -- 6.6.Detection of Meter and Downbeat -- 7.1.Dynamic Time Warping -- 7.1.1.Example -- 7.1.2.Common Variants -- 7.1.3.Optimizations -- 7.2.Audio-to-Audio Alignment -- 7.2.1.Ground Truth Data for Evaluation -- 7.3.Audio-to-Score Alignment -- 7.3.1.Real-Time Systems M -- 7.3.2.Non-Real-Time Systems -- Contents note continued: 8.1.Musical Genre Classification -- 8.1.1.Musical Genre -- 8.1.2.Feature Extraction -- 8.1.3.Classification -- 8.2.Related Research Fields -- 8.2.1.Music Similarity Detection -- 8.2.2.Mood Classification -- 8.2.3.Instrument Recognition -- 9.1.Fingerprint Extraction -- 9.2.Fingerprint Matching -- 9.3.Fingerprinting System: Example -- 10.1.Musical Communication -- 10.1.1.Score -- 10.1.2.Music Performance -- 10.1.3.Production -- 10.1.4.Recipient -- 10.2.Music Performance Analysis -- 10.2.1.Analysis Data -- 10.2.2.Research Results -- A.1.Identity -- A.2.Commutativity -- A.3.Associativity -- A.4.Distributivity -- A.5.Circularity -- B.1.Properties of the Fourier Transformation -- B.1.1.Inverse Fourier Transform -- B.1.2.Superposition -- B.1.3.Convolution and Multiplication -- B.1.4.Parseval's Theorem -- B.1.5.Time and Frequency Shift -- B.1.6.Symmetry -- B.1.7.Time and Frequency Scaling -- B.1.8.Derivatives -- B.2.Spectrum of Example Time Domain Signals -- Contents note continued: B.2.1.Delta Function -- B.2.2.Constant -- B.2.3.Cosine -- B.2.4.Rectangular Window -- B.2.5.Delta Pulse -- B.3.Transformation of Sampled Time Signals -- B.4.Short Time Fourier Transform of Continuous Signals -- B.4.1.Window Functions -- B.5.Discrete Fourier Transform -- B.5.1.Window Functions -- B.5.2.Fast Fourier Transform -- C.1.Computation of the Transformation Matrix -- C.2.Interpretation of the Transformation Matrix -- D.1.Software Frameworks and Applications -- D.1.1.Marsyas -- D.1.2.CLAM -- D.1.3.jMIR -- D.1.4.CoMIRVA -- D.1.5.Sonic Visualiser -- D.2.Software Libraries and Toolboxes -- D.2.1.Feature Extraction -- D.2.2.Plugin Interfaces -- D.2.3.Other Software. |
| Record Nr. | UNINA-9910141368403321 |
|
Lerch Alexander
|
||
| Hoboken, New Jersey : , : Wiley, , c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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An introduction to audio content analysis : applications in signal processing and music informatics / / Alexander Lerch
| An introduction to audio content analysis : applications in signal processing and music informatics / / Alexander Lerch |
| Autore | Lerch Alexander |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , c2012 |
| Descrizione fisica | 1 online resource (272 p.) |
| Disciplina |
006.45
621.3822 |
| Soggetto topico |
Computer sound processing
Computational auditory scene analysis Content analysis (Communication) - Data processing |
| ISBN |
1-283-80405-0
1-118-39350-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Machine generated contents note: 1.1.Audio Content -- 1.2.A Generalized Audio Content Analysis System -- 2.1.Audio Signals -- 2.1.1.Periodic Signals -- 2.1.2.Random Signals -- 2.1.3.Sampling and Quantization -- 2.1.4.Statistical Signal Description -- 2.2.Signal Processing -- 2.2.1.Convolution -- 2.2.2.Block-Based Processing -- 2.2.3.Fourier Transform -- 2.2.4.Constant Q Transform -- 2.2.5.Auditory Filterbanks -- 2.2.6.Correlation Function -- 2.2.7.Linear Prediction -- 3.1.Audio Pre-Processing -- 3.1.1.Down-Mixing -- 3.1.2.DC Removal -- 3.1.3.Normalization -- 3.1.4.Down-Sampling -- 3.1.5.Other Pre-Processing Options -- 3.2.Statistical Properties -- 3.2.1.Arithmetic Mean -- 3.2.2.Geometric Mean -- 3.2.3.Harmonic Mean -- 3.2.4.Generalized Mean -- 3.2.5.Centroid -- 3.2.6.Variance and Standard Deviation -- 3.2.7.Skewness -- 3.2.8.Kurtosis -- 3.2.9.Generalized Central Moments -- 3.2.10.Quantiles and Quantile Ranges -- 3.3.Spectral Shape -- 3.3.1.Spectral Rolloff --
Contents note continued: 3.3.2.Spectral Flux -- 3.3.3.Spectral Centroid -- 3.3.4.Spectral Spread -- 3.3.5.Spectral Decrease -- 3.3.6.Spectral Slope -- 3.3.7.Mel Frequency Cepstral Coefficients -- 3.4.Signal Properties -- 3.4.1.Tonalness -- 3.4.2.Autocorrelation Coefficients -- 3.4.3.Zero Crossing Rate -- 3.5.Feature Post-Processing -- 3.5.1.Derived Features -- 3.5.2.Normalization and Mapping -- 3.5.3.Subfeatures -- 3.5.4.Feature Dimensionality Reduction -- 4.1.Human Perception of Intensity and Loudness -- 4.2.Representation of Dynamics in Music -- 4.3.Features -- 4.3.1.Root Mean Square -- 4.4.Peak Envelope -- 4.5.Psycho-Acoustic Loudness Features -- 4.5.1.EBU R128 -- 5.1.Human Perception of Pitch -- 5.1.1.Pitch Scales -- 5.1.2.Chroma Perception -- 5.2.Representation of Pitch in Music -- 5.2.1.Pitch Classes and Names -- 5.2.2.Intervals -- 5.2.3.Root Note, Mode, and Key -- 5.2.4.Chords and Harmony -- 5.2.5.The Frequency of Musical Pitch -- 5.3.Fundamental Frequency Detection -- Contents note continued: 5.3.1.Detection Accuracy -- 5.3.2.Pre-Processing -- 5.3.3.Monophonic Input Signals -- 5.3.4.Polyphonic Input Signals -- 5.4.Tuning Frequency Estimation -- 5.5.Key Detection -- 5.5.1.Pitch Chroma -- 5.5.2.Key Recognition -- 5.6.Chord Recognition -- 6.1.Human Perception of Temporal Events -- 6.1.1.Onsets -- 6.1.2.Tempo and Meter -- 6.1.3.Rhythm -- 6.1.4.Timing -- 6.2.Representation of Temporal Events in Music -- 6.2.1.Tempo and Time Signature -- 6.2.2.Note Value -- 6.3.Onset Detection -- 6.3.1.Novelty Function -- 6.3.2.Peak Picking -- 6.3.3.Evaluation -- 6.4.Beat Histogram -- 6.4.1.Beat Histogram Features -- 6.5.Detection of Tempo and Beat Phase -- 6.6.Detection of Meter and Downbeat -- 7.1.Dynamic Time Warping -- 7.1.1.Example -- 7.1.2.Common Variants -- 7.1.3.Optimizations -- 7.2.Audio-to-Audio Alignment -- 7.2.1.Ground Truth Data for Evaluation -- 7.3.Audio-to-Score Alignment -- 7.3.1.Real-Time Systems M -- 7.3.2.Non-Real-Time Systems -- Contents note continued: 8.1.Musical Genre Classification -- 8.1.1.Musical Genre -- 8.1.2.Feature Extraction -- 8.1.3.Classification -- 8.2.Related Research Fields -- 8.2.1.Music Similarity Detection -- 8.2.2.Mood Classification -- 8.2.3.Instrument Recognition -- 9.1.Fingerprint Extraction -- 9.2.Fingerprint Matching -- 9.3.Fingerprinting System: Example -- 10.1.Musical Communication -- 10.1.1.Score -- 10.1.2.Music Performance -- 10.1.3.Production -- 10.1.4.Recipient -- 10.2.Music Performance Analysis -- 10.2.1.Analysis Data -- 10.2.2.Research Results -- A.1.Identity -- A.2.Commutativity -- A.3.Associativity -- A.4.Distributivity -- A.5.Circularity -- B.1.Properties of the Fourier Transformation -- B.1.1.Inverse Fourier Transform -- B.1.2.Superposition -- B.1.3.Convolution and Multiplication -- B.1.4.Parseval's Theorem -- B.1.5.Time and Frequency Shift -- B.1.6.Symmetry -- B.1.7.Time and Frequency Scaling -- B.1.8.Derivatives -- B.2.Spectrum of Example Time Domain Signals -- Contents note continued: B.2.1.Delta Function -- B.2.2.Constant -- B.2.3.Cosine -- B.2.4.Rectangular Window -- B.2.5.Delta Pulse -- B.3.Transformation of Sampled Time Signals -- B.4.Short Time Fourier Transform of Continuous Signals -- B.4.1.Window Functions -- B.5.Discrete Fourier Transform -- B.5.1.Window Functions -- B.5.2.Fast Fourier Transform -- C.1.Computation of the Transformation Matrix -- C.2.Interpretation of the Transformation Matrix -- D.1.Software Frameworks and Applications -- D.1.1.Marsyas -- D.1.2.CLAM -- D.1.3.jMIR -- D.1.4.CoMIRVA -- D.1.5.Sonic Visualiser -- D.2.Software Libraries and Toolboxes -- D.2.1.Feature Extraction -- D.2.2.Plugin Interfaces -- D.2.3.Other Software. |
| Record Nr. | UNINA-9910830665403321 |
|
Lerch Alexander
|
||
| Hoboken, New Jersey : , : Wiley, , c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Introduction to Audio Processing / / by Mads G. Christensen
| Introduction to Audio Processing / / by Mads G. Christensen |
| Autore | Christensen Mads G |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XXXI, 224 p. 187 illus., 76 illus. in color.) |
| Disciplina | 006.45 |
| Soggetto topico |
Signal processing
Image processing Speech processing systems Acoustical engineering User interfaces (Computer systems) Application software Signal, Image and Speech Processing Engineering Acoustics User Interfaces and Human Computer Interaction Computer Appl. in Social and Behavioral Sciences |
| ISBN | 3-030-11781-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- What is Sound? -- The Wave Equation -- Digital Audio Signals -- What Are Filters? -- Comb Filters and Periodic Signals -- More About Filters -- The Fourier Transform -- Audio Effects -- Spatial Effects -- Audio Equalizers -- Dynamic Range Control -- Pitch Estimation -- Appendix -- Bibliography. |
| Record Nr. | UNINA-9910337627103321 |
|
Christensen Mads G
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
