UID:
almahu_9949865526102882
Umfang:
1 online resource :
,
illustrations (black and white).
ISBN:
9781315353838
,
1315353830
,
9781498719575
,
1498719570
,
9781315334776
,
1315334771
,
9781315370996
,
1315370999
Serie:
Chapman & Hall/CRC computer science and data analysis series ; 23
Inhalt:
Annotation
Anmerkung:
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- 1: Introduction -- 1.1 Background and Motivation -- 1.2 Content, Target Audience, Prerequisites, Exercises, and Complementary Material -- 1.3 Book Overview -- 1.4 Chapter Summaries -- 1.5 Course Examples -- 1.6 Authors and Editors -- Bibliography -- I: Music and Audio -- 2: The Musical Signal: Physically and Psychologically -- 2.1 Introduction -- 2.2 The Tonal Quality: Pitch- the First Moment -- 2.2.1 Introduction -- 2.2.2 Pure and Complex Tones on a Vibrating String -- 2.2.3 Intervals and Musical Tone Height -- 2.2.4 Musical Notation and Naming of Pitches and Intervals -- 2.2.5 The Mel Scale -- 2.2.6 Fourier Transform -- 2.2.7 Correlation Analysis -- 2.2.8 Fluctuating Pitch and Frequency Modulation -- 2.2.9 Simultaneous Pitches -- 2.2.10 Other Sounds with and without Pitch Percepts -- 2.3 Volume -- the Second Moment -- 2.3.1 Introduction -- 2.3.2 The Physical Basis: Sound Waves in Air -- 2.3.3 Scales for the Subjective Perception of the Volume -- 2.3.4 Amplitude Modulation -- 2.4 Timbre -- the Third Moment -- 2.4.1 Uncertainty Principle -- 2.4.2 Gabor Transform and Spectrogram -- 2.4.3 Application of the Gabor Transform -- 2.4.4 Formants, Vowels, and Characteristic Timbres of Voices and Instruments -- 2.4.5 Transients -- 2.4.6 Sound Fluctuations and Timbre -- 2.4.7 Physical Model for the Timbre of Wind Instruments -- 2.5 Duration -- the Fourth Moment -- 2.5.1 Integration Times and Temporal Resolvability -- 2.5.2 Time Structure in Music: Rhythm and Measure -- 2.5.3 Wavelets and Scalograms -- 2.6 Further Reading -- 2.7 Exercises -- Bibliography -- 3: Musical Structures and Their Perception -- 3.1 Introduction -- 3.2 Scales and Keys -- 3.2.1 Clefs -- 3.2.2 Diatonic and Chromatic Scales -- 3.2.3 Other Scales -- 3.3 Gestalt and Auditory Scene Analysis.
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3.4 Musical Textures from Monophony to Polyphony -- 3.5 Polyphony and Harmony -- 3.5.1 Dichotomy of Consonant and Dissonant Intervals -- 3.5.2 Consonant and Dissonant Intervals and Tone Progression -- 3.5.3 Elementary Counterpoint -- 3.5.4 Chords -- 3.5.5 Modulations -- 3.6 Time Structures of Music -- 3.6.1 Note Values -- 3.6.2 Measure -- 3.6.3 Meter -- 3.6.4 Rhythm -- 3.7 Elementary Theory of Form -- 3.8 Further Reading -- Bibliography -- 4: Digital Filters and Spectral Analysis -- 4.1 Introduction -- 4.2 Continuous-Time, Discrete-Time, and Digital Signals -- 4.3 Discrete-Time Systems -- 4.3.1 Parametric LTI Systems -- 4.3.2 Digital Filters and Filter Design -- 4.4 Spectral Analysis Using the Discrete Fourier Transform -- 4.4.1 The Discrete Fourier Transform -- 4.4.2 Frequency Resolution and Zero Padding -- 4.4.3 Short-Time Spectral Analysis -- 4.5 The Constant-Q Transform -- 4.6 Filter Banks for Short-Time Spectral Analysis -- 4.6.1 Uniform Filter Banks -- 4.6.2 Nonuniform Filter Banks -- 4.7 The Cepstrum -- 4.8 Fundamental Frequency Estimation -- 4.9 Further Reading -- Bibliography -- 5: Signal-Level Features -- 5.1 Introduction -- 5.2 Timbre Features -- 5.2.1 Time-Domain Features -- 5.2.2 Frequency-Domain Features -- 5.2.3 Mel Frequency Cepstral Coefficients -- 5.3 Harmony Features -- 5.3.1 Chroma Features -- 5.3.2 Chroma Energy Normalized Statistics -- 5.3.3 Timbre-Invariant Chroma Features -- 5.3.4 Characteristics of Partials -- 5.4 Rhythmic Features -- 5.4.1 Features for Onset Detection -- 5.4.2 Phase-Domain Characteristics -- 5.4.3 Fluctuation Patterns -- 5.5 Further Reading -- Bibliography -- 6: Auditory Models -- 6.1 Introduction -- 6.2 Auditory Periphery -- 6.3 The Meddis Model of the Auditory Periphery -- 6.3.1 Outer and Middle Ear -- 6.3.2 Basilar Membrane -- 6.3.3 Inner Hair Cells -- 6.3.4 Auditory Nerve Synapse.
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6.3.5 Auditory Nerve Activity -- 6.4 Pitch Estimation Using Auditory Models -- 6.4.1 Autocorrelation Models -- 6.4.2 Pitch Extraction in the Brain -- 6.5 Further Reading -- Bibliography -- 7: Digital Representation of Music -- 7.1 Introduction -- 7.2 From Sheet to File -- 7.2.1 Optical Music Recognition -- 7.2.2 abc Music Notation -- 7.2.3 Musical Instrument Digital Interface -- 7.2.4 MusicXML 3.0 -- 7.3 From Signal to File -- 7.3.1 Pulse Code Modulation and Raw Audio Format -- 7.3.2 WAVE File Format -- 7.3.3 MP3 Compression -- 7.4 From File to Sheet -- 7.4.1 MusicTeX Typesetting -- 7.4.2 Transcription Tools -- 7.5 From File to Signal -- 7.6 Further Reading -- Bibliography -- 8: Music Data: Beyond the Signal Level -- 8.1 Introduction -- 8.2 From the Signal Level to Semantic Features -- 8.2.1 Types of Semantic Features -- 8.2.2 Deriving Semantic Features -- 8.2.3 Discussion -- 8.3 Symbolic Features -- 8.4 Music Scores -- 8.5 Social Web -- 8.5.1 Social Tags -- 8.5.2 Shared Playlists -- 8.5.3 Listening Activity -- 8.6 Music Databases -- 8.7 Lyrics -- 8.8 Concluding Remarks -- Bibliography -- II: Methods -- 9: Statistical Methods -- 9.1 Introduction -- 9.2 Probability -- 9.2.1 Theory -- 9.2.2 Empirical Analogues -- 9.3 Random Variables -- 9.3.1 Theory -- 9.3.2 Empirical Analogues -- 9.4 Characterization of Random Variables -- 9.4.1 Theory -- 9.4.2 Empirical Analogues -- 9.4.3 Important Univariate Distributions -- 9.5 Random Vectors -- 9.5.1 Theory -- 9.5.2 Empirical Analogues -- 9.6 Estimators of Unknown Parameters and Their Properties -- 9.7 Testing Hypotheses on Unknown Parameters -- 9.8 Modeling of the Relationship between Variables -- 9.8.1 Regression -- 9.8.2 Time Series Models -- 9.8.3 Towards Smaller and Easier to Handle Models -- 9.9 Further Reading -- Bibliography -- 10: Optimization -- 10.1 Introduction -- 10.2 Basic Concepts.
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10.3 Single-Objective Problems -- 10.3.1 Binary Feasible Sets -- 10.3.2 Continuous Feasible Sets -- 10.3.3 Compound Feasible Sets -- 10.4 Multi-Objective Problems -- 10.5 Further Reading -- Bibliography -- 11: Unsupervised Learning -- 11.1 Introduction -- 11.2 Distance Measures and Cluster Distinction -- 11.3 Agglomerative Hierarchical Clustering -- 11.3.1 Agglomerative Hierarchical Methods -- 11.3.2 Ward Method -- 11.3.3 Visualization -- 11.4 Partition Methods -- 11.4.1 k-Means Methods -- 11.4.2 Self-Organizing Maps -- 11.5 Clustering Features -- 11.6 Independent Component Analysis -- 11.7 Further Reading -- Bibliography -- 12: Supervised Classification -- 12.1 Introduction -- 12.2 Supervised Learning and Classification -- 12.3 Targets of Classification -- 12.4 Selected Classification Methods -- 12.4.1 Bayes and Approximate Bayes Methods -- 12.4.2 Nearest Neighbor Prediction -- 12.4.3 Decision Trees -- 12.4.4 Support Vector Machines -- 12.4.5 Ensemble Methods: Bagging -- 12.4.6 Neural Networks -- 12.5 Interpretation of Classification Results -- 12.6 Further Reading -- Bibliography -- 13: Evaluation -- 13.1 Introduction -- 13.2 Resampling -- 13.2.1 Resampling Methods -- 13.2.2 Hold-Out -- 13.2.3 Cross-Validation -- 13.2.4 Bootstrap -- 13.2.5 Subsampling -- 13.2.6 Properties and Recommendations -- 13.3 Evaluation Measures -- 13.3.1 Loss-Based Performance -- 13.3.2 Confusion Matrix -- 13.3.3 Common Performance Measures Based on the Confusion Matrix -- 13.3.4 Measures for Imbalanced Sets -- 13.3.5 Evaluation of Aggregated Predictions -- 13.3.6 Measures beyond Classification Performance -- 13.4 Hyperparameter Tuning: Nested Resampling -- 13.5 Tests for Comparing Classifiers -- 13.5.1 McNemar Test -- 13.5.2 Pairwise t-Test Based on B Independent Test Data Sets -- 13.5.3 Comparison of Many Classifiers -- 13.6 Multi-Objective Evaluation.
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13.7 Further Reading -- Bibliography -- 14: Feature Processing -- 14.1 Introduction -- 14.2 Preprocessing -- 14.2.1 Transforms of Feature Domains -- 14.2.2 Normalization -- 14.2.3 Missing Values -- 14.2.4 Harmonization of the Feature Matrix -- 14.3 Processing of Feature Dimension -- 14.4 Processing of Time Dimension -- 14.4.1 Sampling and Order-Independent Statistics -- 14.4.2 Order-Dependent Statistics Based on Time Series Analysis -- 14.4.3 Frame Selection Based on Musical Structure -- 14.5 Automatic Feature Construction -- 14.6 A Note on the Evaluation of Feature Processing -- 14.7 Further Reading -- Bibliography -- 15: Feature Selection -- 15.1 Introduction -- 15.2 Definitions -- 15.3 The Scope of Feature Selection -- 15.4 Design Steps and Categorization of Methods -- 15.5 Ways to Measure Relevance of Features -- 15.5.1 Correlation-Based Relevance -- 15.5.2 Comparison of Feature Distributions -- 15.5.3 Relevance Derived from Information Theory -- 15.6 Examples for Feature Selection Algorithms -- 15.6.1 Relief -- 15.6.2 Floating Search -- 15.6.3 Evolutionary Search -- 15.7 Multi-Objective Feature Selection -- 15.8 Further Reading -- Bibliography -- III: Applications -- 16: Segmentation -- 16.1 Introduction -- 16.2 Onset Detection -- 16.2.1 Definition -- 16.2.2 Detection Strategies -- 16.2.3 Goodness of Onset Detection -- 16.3 Tone Phases -- 16.3.1 Reasons for Clustering -- 16.3.2 The Clustering Process -- 16.3.3 Refining the Clustering Process -- 16.4 Musical Structure Analysis -- 16.5 Concluding Remarks -- 16.6 Further Reading -- Bibliography -- 17: Transcription -- 17.1 Introduction -- 17.2 Data -- 17.3 Musical Challenges: Partials, Vibrato, and Noise -- 17.4 Statistical Challenge: Piecewise Local Stationarity -- 17.5 Transcription Scheme -- 17.5.1 Separation of the Relevant Part of Music -- 17.5.2 Estimation of Fundamental Frequency.
Weitere Ausg.:
Print version : ISBN 9781498719568
Sprache:
Englisch
URL:
https://www.taylorfrancis.com/books/9781315370996
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