Music data analysis: foundations and applications
Gespeichert in:
| Weitere beteiligte Personen: | , , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press, Taylor & Franics Group
[2017]
|
| Schriftenreihe: | Chapman & Hall/CRC computer science and data analysis series
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029266433&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xviii, 675 Seiten Illustrationen, Diagramme |
| ISBN: | 9781498719568 |
Internformat
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| 245 | 1 | 0 | |a Music data analysis |b foundations and applications |c edited by Claus Weihs, Dietmar Jannach, Igor Vatolkin, Günter Rudolph |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press, Taylor & Franics Group |c [2017] | |
| 300 | |a xviii, 675 Seiten |b Illustrationen, Diagramme | ||
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| 337 | |b n |2 rdamedia | ||
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| 700 | 1 | |a Vatolkin, Igor |d 1980- |0 (DE-588)173854303 |4 edt | |
| 700 | 1 | |a Rudolph, Günter |0 (DE-588)1031293884 |4 edt | |
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Datensatz im Suchindex
| _version_ | 1819278084839309312 |
|---|---|
| adam_text | Contents
1 Introduction 1
1.1 Background and Motivation 1
1.2 Content, Target Audience, Prerequisites, Exercises,
and Complementary Material 2
1.3 Book Overview 3
1.4 Chapter Summaries 3
1.5 Course Examples 8
1.6 Authors and Editors 9
Bibliography 11
1 Music and Audio 13
2 The Musical Signal: Physically and Psychologically 15
2.1 Introduction 15
2.2 The Tonal Quality: Pitch — the First Moment 16
2.2.1 Introduction 16
2.2.2 Pure and Complex Tones on a Vibrating String 17
2.2.3 Intervals and Musical Tone Height 22
2.2.4 Musical Notation and Naming of Pitches and Intervals 26
2.2.5 The Mel Scale 29
2.2.6 Fourier Transform 31
2.2.7 Correlation Analysis 34
2.2.8 Fluctuating Pitch and Frequency Modulation 36
2.2.9 Simultaneous Pitches 37
2.2.10 Other Sounds with and without Pitch Percepts 39
2.3 Volume — the Second Moment 41
2.3.1 Introduction 41
2.3.2 The Physical Basis: Sound Waves in Air 41
2.3.3 Scales for the Subjective Perception of the Volume 46
2.3.4 Amplitude Modulation 49
2.4 Timbre — the Third Moment 50
2.4.1 Uncertainty Principle 51
2.4.2 Gabor Transform and Spectrogram 52
2.4.3 Application of the Gabor Transform 53
vii
Vlll
CONTENTS
2.4.4 Formants, Vowels, and Characteristic Timbres of Voices and
Instruments 54
2.4.5 Transients 56
2.4.6 Sound Fluctuations and Timbre 58
2.4.7 Physical Model for the Timbre of Wind Instruments 58
2.5 Duration — the Fourth Moment 62
2.5.1 Integration Times and Temporal Resolvability 62
2.5.2 Time Structure in Music: Rhythm and Measure 63
2.5.3 Wavelets and Scalograms 63
2.6 Further Reading 66
2.7 Exercises 66
Bibliography 66
3 Musical Structures and Their Perception 69
3.1 Introduction 69
3.2 Scales and Keys 69
3.2.1 Clefs 69
3.2.2 Diatonic and Chromatic Scales 70
3.2.3 Other Scales 72
3.3 Gestalt and Auditory Scene Analysis 74
3.4 Musical Textures from Monophony to Polyphony 77
3.5 Polyphony and Harmony 77
3.5.1 Dichotomy of Consonant and Dissonant Intervals 78
3.5.2 Consonant and Dissonant Intervals and Tone Progression 81
3.5.3 Elementary Counterpoint 82
3.5.4 Chords 85
3.5.5 Modulations 94
3.6 Time Structures of Music 95
3.6.1 Note Values 95
3.6.2 Measure 97
3.6.3 Meter 97
3.6.4 Rhythm 99
3.7 Elementary Theory of Form 100
3.8 Further Reading 107
Bibliography 108
4 Digital Filters and Spectral Analysis 111
4.1 Introduction 111
4.2 Continuous-Time, Discrete-Time, and Digital Signals 111
4.3 Discrete-Time Systems 112
4.3.1 Parametric LTI Systems 116
4.3.2 Digital Filters and Filter Design 118
4.4 Spectral Analysis Using the Discrete Fourier Transform 123
4.4.1 The Discrete Fourier Transform 123
4.4.2 Frequency Resolution and Zero Padding 127
viii
CONTENTS
IX
4.4.3 Short-Time Spectral Analysis 129
4.5 The Constant-Q Transform 130
4.6 Filter Banks for Short-Time Spectral Analysis 131
4.6.1 Uniform Filter Banks 132
4.6.2 Nonuniform Filter Banks 135
4.7 The Cepstrum 136
4.8 Fundamental Frequency Estimation 138
4.9 Further Reading 140
Bibliography 141
5 Signal-Level Features 145
5.1 Introduction 145
5.2 Timbre Features 146
5.2.1 Time-Domain Features 146
5.2.2 Frequency-Domain Features 147
5.2.3 Mel Frequency Cepstral Coefficients 151
5.3 Harmony Features 153
5.3.1 Chroma Features 153
5.3.2 Chroma Energy Normalized Statistics 154
5.3.3 Timbre-Invariant Chroma Features 155
5.3.4 Characteristics of Partials 156
5.4 Rhythmic Features 157
5.4.1 Features for Onset Detection 157
5.4.2 Phase-Domain Characteristics 159
5.4.3 Fluctuation Patterns 160
5.5 Further Reading 162
Bibliography 162
6 Auditory Models 165
6.1 Introduction 165
6.2 Auditory Periphery 166
6.3 The Meddis Model of the Auditory Periphery 167
6.3.1 Outer and Middle Ear 168
6.3.2 Basilar Membrane 169
6.3.3 Inner Hair Cells 169
6.3.4 Auditory Nerve Synapse 169
6.3.5 Auditory Nerve Activity 170
6.4 Pitch Estimation Using Auditory Models 170
6.4.1 Autocorrelation Models 170
6.4.2 Pitch Extraction in the Brain 171
6.5 Further Reading 172
Bibliography 173
IX
X
CONTENTS
I Digital Representation of Music 177
7.1 Introduction 177
7.2 From Sheet to File 178
7.2.1 Optical Music Recognition 178
7.2.2 abc Music Notation 179
7.2.3 Musical Instrument Digital Interface 180
7.2.4 MusicXML 3.0 184
7.3 From Signal to File 186
7.3.1 Pulse Code Modulation and Raw Audio Format 187
7.3.2 WAVE File Format 189
7.3.3 MP3 Compression 190
7.4 From File to Sheet 193
7.4.1 MusicTeX Typesetting 194
7.4.2 Transcription Tools 195
7.5 From File to Signal 195
7.6 Further Reading 196
Bibliography 196
8 Music Data: Beyond the Signal Level 197
8.1 Introduction 197
8.2 From the Signal Level to Semantic Features 198
8.2.1 Types of Semantic Features 198
8.2.2 Deriving Semantic Features 199
8.2.3 Discussion 200
8.3 Symbolic Features 201
8.4 Music Scores 203
8.5 Social Web 204
8.5.1 Social Tags 205
8.5.2 Shared Playlists 205
8.5.3 Listening Activity 207
8.6 Music Databases 208
8.7 Lyrics 209
8.8 Concluding Remarks 212
Bibliography 212
II Methods 217
9 Statistical Methods 219
9.1 Introduction 219
9.2 Probability 219
9.2.1 Theory 219
9.2.2 Empirical Analogues 222
9.3 Random Variables 223
9.3.1 Theory 223
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CONTENTS
xi
9.3.2 Empirical Analogues 225
9.4 Characterization of Random Variables 227
9.4.1 Theory 227
9.4.2 Empirical Analogues 229
9.4.3 Important Univariate Distributions 233
9.5 Random Vectors 236
9.5.1 Theory 236
9.5.2 Empirical Analogues 239
9.6 Estimators of Unknown Parameters and Their Properties 242
9.7 Testing Hypotheses on Unknown Parameters 244
9.8 Modeling of the Relationship between Variables 248
9.8.1 Regression 248
9.8.2 Time Series Models 252
9.8.3 Towards Smaller and Easier to Handle Models 259
9.9 Further Reading 262
Bibliography 262
10 Optimization 263
10.1 Introduction 263
10.2 Basic Concepts 264
10.3 Single-Objective Problems 266
10.3.1 Binary Feasible Sets 266
10.3.2 Continuous Feasible Sets 271
10.3.3 Compound Feasible Sets 276
10.4 Multi-Objective Problems 276
10.5 Further Reading 281
Bibliography 281
11 Unsupervised Learning 283
11.1 Introduction 283
11.2 Distance Measures and Cluster Distinction 284
11.3 Agglomerative Hierarchical Clustering 287
11.3.1 Agglomerative Hierarchical Methods 287
11.3.2 Ward Method 289
11.3.3 Visualization 290
11.4 Partition Methods 291
11.4.1 k-Means Methods 291
11.4.2 Self-Organizing Maps 293
11.5 Clustering Features 297
11.6 Independent Component Analysis 297
11.7 Further Reading 301
Bibliography 302
xi
xii______________________________________________________________CONTENTS
12 Supervised Classification 303
12.1 Introduction 303
12.2 Supervised Learning and Classification 304
12.3 Targets of Classification 305
12.4 Selected Classification Methods 306
12.4.1 Bayes and Approximate Bayes Methods 307
12.4.2 Nearest Neighbor Prediction 310
12.4.3 Decision Trees 312
12.4.4 Support Vector Machines 314
12.4.5 Ensemble Methods: Bagging 319
12.4.6 Neural Networks 320
12.5 Interpretation of Classification Results 324
12.6 Further Reading 325
Bibliography 326
13 Evaluation 329
13.1 Introduction 3 29
13.2 Resampling 332
13.2.1 Resampling Methods 334
13.2.2 Hold-Out 334
13.2.3 Cross-Validation 335
13.2.4 Bootstrap 336
13.2.5 Subsampling 338
13.2.6 Properties and Recommendations 338
13.3 Evaluation Measures 339
13.3.1 Loss-Based Performance 339
13.3.2 Confusion Matrix 340
13.3.3 Common Performance Measures Based on the Confusion
Matrix 341
13.3.4 Measures for Imbalanced Sets 343
13.3.5 Evaluation of Aggregated Predictions 345
13.3.6 Measures beyond Classification Performance 347
13.4 Hyperparameter Tuning: Nested Resampling 352
13.5 Tests for Comparing Classifiers 354
13.5.1 McNemar Test 354
13.5.2 Pairwise t-Test Based on B Independent Test Data Sets 356
13.5.3 Comparison of Many Classifiers 357
13.6 Multi-Objective Evaluation 359
13.7 Further Reading 360
Bibliography 361
14 Feature Processing 365
14.1 Introduction 365
14.2 Preprocessing 367
14.2.1 Transforms of Feature Domains 367
xii
CONTENTS xiii
14.2.2 Normalization 368
14.2.3 Missing Values 371
14.2.4 Harmonization of the Feature Matrix 372
14.3 Processing of Feature Dimension 373
14.4 Processing of Time Dimension 374
14.4.1 Sampling and Order-Independent Statistics 374
14.4.2 Order-Dependent Statistics Based on Time Series Analysis 375
14.4.3 Frame Selection Based on Musical Structure 377
14.5 Automatic Feature Construction 380
14.6 A Note on the Evaluation of Feature Processing 383
14.7 Further Reading 385
Bibliography 385
15 Feature Selection 389
15.1 Introduction 389
15.2 Definitions 390
15.3 The Scope of Feature Selection 393
15.4 Design Steps and Categorization of Methods 394
15.5 Ways to Measure Relevance of Features 395
15.5.1 Correlation-Based Relevance 395
15.5.2 Comparison of Feature Distributions 396
15.5.3 Relevance Derived from Information Theory 397
15.6 Examples for Feature Selection Algorithms 398
15.6.1 Relief 398
15.6.2 Floating Search 400
15.6.3 Evolutionary Search 400
15.7 Multi-Objective Feature Selection 402
15.8 Further Reading 404
Bibliography 405
III Applications 409
16 Segmentation 411
16.1 Introduction 411
16.2 Onset Detection 412
16.2.1 Definition 412
16.2.2 Detection Strategies 413
16.2.3 Goodness of Onset Detection 419
16.3 Tone Phases 422
16.3.1 Reasons for Clustering 422
16.3.2 The Clustering Process 422
16.3.3 Refining the Clustering Process 425
16.4 Musical Structure Analysis 425
16.5 Concluding Remarks 428
xiii
XIV
CONTENTS
16.6 Further Reading 429
Bibliography 430
17 Transcription 433
17.1 Introduction 433
17.2 Data 434
17.3 Musical Challenges: Partials, Vibrato, and Noise 434
17.4 Statistical Challenge: Piecewise Local Stationarity 435
17.5 Transcription Scheme 436
17.5.1 Separation of the Relevant Part of Music 436
17.5.2 Estimation of Fundamental Frequency 436
17.5.3 Classification of Notes, Silence, and Noise 440
17.5.4 Estimation of Relative Length of Notes and Meter 442
17.5.5 Estimation of the Key 443
17.5.6 Final Transcription into Sheet Music 443
17.6 Software 443
17.7 Concluding Remarks 444
17.8 Further Reading 445
Bibliography 446
18 Instrument Recognition 451
18.1 Introduction 451
18.2 Types of Instrument Recognition 453
18.3 Taxonomy Design 454
18.4 Example of Instrument Recognition 456
18.4.1 Labeled Data 456
18.4.2 Taxonomy Design 457
18.4.3 Feature Extraction and Processing 458
18.4.4 Feature Selection and Supervised Classification 459
18.4.5 Evaluation 460
18.4.6 Summary of Example 464
18.5 Concluding Remarks 464
18.6 Further Reading 464
Bibliography 465
19 Chord Recognition 469
19.1 Introduction 469
19.2 Chord Dictionary 470
19.3 Chroma or Pitch Class Profile Extraction 471
19.3.1 Computation Using the Short-Time Fourier Transform 472
19.3.2 Computation Using the Constant-Q Transform 472
19.3.3 Influence of Timbre on the Chroma/PCP 474
19.4 Chord Representation 476
19.4.1 Knowledge-Driven Approach 476
19.4.2 Data-Driven Approach 476
xiv
CONTENTS
xv
19.5 Frame-Based System for Chord Recognition 477
19.5.1 Knowledge-Driven Approach 477
19.5.2 Data-Driven Approach 479
19.5.3 Chord Fragmentation 479
19.6 Hidden Markov Model-Based System for Chord Recognition 479
19.6.1 Knowledge-Driven Transition Probabilities 481
19.6.2 Data-Driven Transition Probabilities 481
19.7 Joint Chord and Key Recognition 483
19.7.1 Key-Only Recognition 484
19.7.2 Joint Chord and Key Recognition 484
19.8 Evaluating the Performances of Chord and Key Estimation 485
19.8.1 Evaluating Segmentation Quality 485
19.8.2 Evaluating Labeling Quality 485
19.9 Concluding Remarks 487
19.10 Further Reading 487
19.10.1 Alternative Audio Signal Representations 488
19.10.2 Alternative Representations of the Chord Labels 488
19.10.3 Taking into Account Other Musical Concepts 488
Bibliography 489
20 Tempo Estimation 493
20.1 Introduction 493
20.2 Definitions 494
20.2.1 Beat 494
20.2.2 Tempo 495
20.2.3 Metrical Levels 496
20.2.4 Automatic Rhythm Estimation 496
20.3 Overall Scheme of Tempo Estimation 498
20.3.1 Feature List Creation 498
20.3.2 Tempo Induction 501
20.4 Evaluation of Tempo Estimation 501
20.5 A Simple Tempo Estimation System 502
20.6 Applications of Automatic Rhythm Estimation 504
20.7 Concluding Remarks 505
20.8 Further Reading 506
Bibliography 506
21 Emotions 511
21.1 Introduction 511
21.1.1 What Are Emotions? 511
21.1.2 Difference between Basic Emotions, Moods, and Emotional
Episodes 512
21.1.3 Personality Differences and Emotion Perception 512
21.2 Theories of Emotions and Models 513
21.2.1 Hevner Clusters of Affective Terms 513
xv
XVI
CONTENTS
21.2.2 Semantic Differential 515
21.2.3 Schubert Clusters 515
21.2.4 Circumplex Word Mapping by Russell 516
21.2.5 Watson—Tellegen Diagram 516
21.3 Speech and Emotion 517
21.4 Music and Emotion 518
21.4.1 Basic Emotions 518
21.4.2 Moods and Other Affective States 520
21.5 Factors of Influence and Features 522
21.5.1 Harmony and Pitch 522
21.5.2 Melody 524
21.5.3 Instrumentation and Timbre 525
21.5.4 Dynamics 525
21.5.5 Tempo and Rhythm 526
21.5.6 Lyrics, Genres, and Social Data 527
21.5.7 Examples: Individual Comparison of Features 528
21.6 Computationally Based Emotion Recognition 530
21.6.1 A Note on Feature Processing 532
21.6.2 Future Challenges 534
21.7 Concluding Remarks 534
21.8 Further Reading 535
Bibliography 535
22 Similarity-Based Organization of Music Collections 541
22.1 Introduction 541
22.2 Learning a Music Similarity Measure 542
22.2.1 Formalizing an Adaptable Model of Music Similarity 543
22.2.2 Modeling Preferences through Distance Constraints 544
22.2.3 Dealing with Inconsistent Constraint Sets 547
22.2.4 Learning Distance Facet Weights 547
22.3 Visualization: Dealing with Projection Errors 550
22.3.1 Popular Projection Techniques 550
22.3.2 Common and Unavoidable Projection Errors 551
22.3.3 Static Visualization of Local Projection Properties 552
22.3.4 Dynamic Visualization of “Wormholes” 553
22.3.5 Combined Visualization of Different Structural Views 555
22.4 Dealing with Changes in the Collection 555
22.4.1 Incremental Structuring Techniques 556
22.4.2 Aligned Projections 556
22.5 Concluding Remarks 558
22.6 Further Reading 558
Bibliography 559
xvi
CONTENTS xvii
23 Music Recommendation 563
23.1 Introduction 563
23.2 Common Recommendation Techniques 564
23.2.1 Collaborative Filtering 564
23.2.2 Content-Based Recommendation 569
23.2.3 Further Knowledge Sources and Hybridization 572
23.3 Specific Aspects of Music Recommendation 574
23.4 Evaluating Recommender Systems 576
23.4.1 Laboratory Studies 576
23.4.2 Offline Evaluation and Accuracy Metrics 576
23.4.3 Beyond Accuracy; Additional Quality Factors 578
23.5 Current Topics and Outlook 581
23.5.1 Context-Aware Recommendation 581
23.5.2 Incorporating Social Web Information 582
23.5.3 Playlist Generation 583
23.6 Concluding Remarks 584
23.7 Further Reading 584
Bibliography 585
24 Automatic Composition 589
24.1 Introduction 589
24.2 Composition 589
24.2.1 What Composers Do 589
24.2.2 Why Automatic Composition? 590
24.2.3 A Short History of Automatic Composition 592
24.3 Principles of Automatic Composition 593
24.3.1 Basic Methods 593
24.3.2 Advanced Methods 599
24.3.3 Evaluation of Automatically Composed Music 603
24.4 Concluding Remarks 603
24.5 Further Reading 603
Bibliography 603
IV Implementation 607
25 Implementation Architectures 609
25.1 Introduction 609
25.2 Architecture Variants and Their Evaluation 610
25.2.1 Personal Player Device Processing 612
25.2.2 Network Server-Based Processing 613
25.2.3 Distributed Architectures 614
25.3 Applications 615
25.3.1 Music Recommendation 615
25.3.2 Music Recognition 616
xvii
xviii_________________________________________________________________CONTENTS
25.4 Novel Applications and Future Development 617
25.5 Concluding Remarks 620
25.6 Further Reading 621
Bibliography 621
26 User Interaction 623
26.1 Introduction 623
26.2 User Input for Music Applications 625
26.2.1 Haptic Input 625
26.2.2 Audio Input 627
26.2.3 Visual and Other Sensor Input 629
26.2.4 Multi-Modal Input 630
26.2.5 Coordination of Inputs from Multiple Users 631
26.3 User Interface Output for Music Applications 631
26.3.1 Audio Presentation 631
26.3.2 Visual Presentation 631
26.3.3 Haptic Presentation 633
26.3.4 Multi-Modal Presentation 634
26.4 Factors Supporting the Interpretation of User Input 635
26.4.1 Role of Context in Music Interaction 635
26.4.2 Impact of Implementation Architectures 636
26.4.3 Influence of Social Interaction and Machine Learning 637
26.5 Concluding Remarks 638
Bibliography 639
27 Hardware Architectures for Music Classification 641
27.1 Introduction 641
27.2 Evaluation Metrics for Hardware Architectures 642
27.2.1 Cost Factors 642
27.2.2 Combined Cost Metrics 643
27.3 Specific Methods for Feature Extraction for Hardware Utilization 644
27.4 Architectures for Digital Signal Processing 644
27.4.1 General Purpose Processor 644
27.4.2 Graphics Processing Unit 648
27.4.3 Digital Signal Processor 651
27.4.4 Application-Specific Instruction Set Processor 654
27.4.5 Dedicated Hardware 654
27.5 Design Space Exploration 658
27.6 Concluding Remarks 661
27.7 Further Reading 662
Bibliography 662
Notation 665
Index 667
xviii
|
| any_adam_object | 1 |
| author2 | Weihs, Claus 1953- Jannach, Dietmar 1973- Vatolkin, Igor 1980- Rudolph, Günter |
| author2_role | edt edt edt edt |
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| author_facet | Weihs, Claus 1953- Jannach, Dietmar 1973- Vatolkin, Igor 1980- Rudolph, Günter |
| building | Verbundindex |
| bvnumber | BV043856238 |
| classification_rvk | LR 57790 ZN 6040 LR 55528 |
| ctrlnum | (OCoLC)968684428 (DE-599)HBZHT019085386 |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik Musikwissenschaft |
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| id | DE-604.BV043856238 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T17:46:56Z |
| institution | BVB |
| isbn | 9781498719568 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029266433 |
| oclc_num | 968684428 |
| open_access_boolean | |
| owner | DE-29T DE-12 DE-20 DE-523 DE-Po75 |
| owner_facet | DE-29T DE-12 DE-20 DE-523 DE-Po75 |
| physical | xviii, 675 Seiten Illustrationen, Diagramme |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | CRC Press, Taylor & Franics Group |
| record_format | marc |
| series2 | Chapman & Hall/CRC computer science and data analysis series |
| spellingShingle | Music data analysis foundations and applications Musik (DE-588)4040802-4 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4040802-4 (DE-588)4123037-1 |
| title | Music data analysis foundations and applications |
| title_auth | Music data analysis foundations and applications |
| title_exact_search | Music data analysis foundations and applications |
| title_full | Music data analysis foundations and applications edited by Claus Weihs, Dietmar Jannach, Igor Vatolkin, Günter Rudolph |
| title_fullStr | Music data analysis foundations and applications edited by Claus Weihs, Dietmar Jannach, Igor Vatolkin, Günter Rudolph |
| title_full_unstemmed | Music data analysis foundations and applications edited by Claus Weihs, Dietmar Jannach, Igor Vatolkin, Günter Rudolph |
| title_short | Music data analysis |
| title_sort | music data analysis foundations and applications |
| title_sub | foundations and applications |
| topic | Musik (DE-588)4040802-4 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Musik Datenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029266433&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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