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E-raamat: Music Data Analysis: Foundations and Applications

Edited by , Edited by (University Klagenfurt, Austria), Edited by , Edited by (TU Dortmund University, Germany)
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Music Data Analysis: Foundations and ApplicationsSuurem pilt
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This volume describes the foundations of music data analysis and the key types of applications that can be built on that analysis. Contributors working in engineering, music, statistics, and computer science from Europe and Colombia (mainly in the Special Interest Group on Music Data Analysis in Dortmund und Bochum, Germany) discuss acoustics, fundamental musical structures, physics and human perception, digital signal processing, the extraction of musical features from the audio signal and other sources, representing music in digital form, modern data analysis and machine learning techniques, and evaluation methods; applications of music data analysis, including music recommendation, automated composition, the detection of emotions, organization of music collections, transcription and segmentation, and instrument, chord, and tempo recognition; and implementation aspects of music data analysis systems, including their architecture, user interface, and hardware-related issues. Basic programming skills in R or MATLAB and music reading skills are recommended. Annotation ©2017 Ringgold, Inc., Portland, OR (protoview.com)

This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects of music data analysis such as architecture, user interface and hardware. It is ideal for use in university classes with an interest in music data analysis. It also could be used in computer science and statistics as well as musicology.

Arvustused

" . . . what makes this book unique is that it covers a much broader range of topics. Not only does it present a concrete tutorial on signal processing and music information retrieval . . . , but it also talks about interesting topics such as emotions, automatic composition, hardware, and others, so readers are sure to find novel information . . . In summary, MusicDataAnalysis is well thought-out and well written. It chooses to emphasize a breadth of topics rather than specialize in specific ones. This book nicely accomplishes its goal of serving as an introductory textbook for music research. It is also a very useful reference and valuable resource for individuals seeking new directions in the field." ~Yupeng Gu, Journal of the American Statistical Association

". . . the book is impressive in its structure, comprehensiveness, clarity and accuracy. . . This text has staked out a specialised interdisciplinary niche, but as a self-contained guide to computational methods for music, I think it unlikely to be surpassed in the near future." ~David Bulger, Australian & New Zealand Journal of Statistics

"Theoretical and practical exercises based on R and MATLAB are provided in the books web site, as well as example data sets. The book is very clearly written, and the style is fairly uniform despite the large number of authors. In sum, a very useful and enjoyable book." ~Ricardo Maronna, Stat Papers

1 Introduction
1(12)
1.1 Background and Motivation
1(1)
1.2 Content, Target Audience, Prerequisites, Exercises, and Complementary Material
2(1)
1.3 Book Overview
3(1)
1.4
Chapter Summaries
3(5)
1.5 Course Examples
8(1)
1.6 Authors and Editors
9(4)
Bibliography
11(2)
I Music and Audio
13(204)
2 The Musical Signal: Physically and Psychologically
15(54)
2.1 Introduction
15(1)
2.2 The Tonal Quality: Pitch -- the First Moment
16(25)
2.2.1 Introduction
16(1)
2.2.2 Pure and Complex Tones on a Vibrating String
17(5)
2.2.3 Intervals and Musical Tone Height
22(4)
2.2.4 Musical Notation and Naming of Pitches and Intervals
26(3)
2.2.5 The Mel Scale
29(2)
2.2.6 Fourier Transform
31(3)
2.2.7 Correlation Analysis
34(2)
2.2.8 Fluctuating Pitch and Frequency Modulation
36(1)
2.2.9 Simultaneous Pitches
37(2)
2.2.10 Other Sounds with and without Pitch Percepts
39(2)
2.3 Volume --- the Second Moment
41(9)
2.3.1 Introduction
41(1)
2.3.2 The Physical Basis: Sound Waves in Air
41(5)
2.3.3 Scales for the Subjective Perception of the Volume
46(3)
2.3.4 Amplitude Modulation
49(1)
2.4 Timbre --- the Third Moment
50(12)
2.4.1 Uncertainty Principle
51(1)
2.4.2 Gabor Transform and Spectrogram
52(1)
2.4.3 Application of the Gabor Transform
53(1)
2.4.4 Formants, Vowels, and Characteristic Timbres of Voices and Instruments
54(2)
2.4.5 Transients
56(2)
2.4.6 Sound Fluctuations and Timbre
58(1)
2.4.7 Physical Model for the Timbre of Wind Instruments
58(4)
2.5 Duration --- the Fourth Moment
62(4)
2.5.1 Integration Times and Temporal Resolvability
62(1)
2.5.2 Time Structure in Music: Rhythm and Measure
63(1)
2.5.3 Wavelets and Scalograms
63(3)
2.6 Further Reading
66(1)
2.7 Exercises
66(3)
Bibliography
66(3)
3 Musical Structures and Their Perception
69(42)
3.1 Introduction
69(1)
3.2 Scales and Keys
69(5)
3.2.1 Clefs
69(1)
3.2.2 Diatonic and Chromatic Scales
70(2)
3.2.3 Other Scales
72(2)
3.3 Gestalt and Auditory Scene Analysis
74(3)
3.4 Musical Textures from Monophony to Polyphony
77(1)
3.5 Polyphony and Harmony
77(18)
3.5.1 Dichotomy of Consonant and Dissonant Intervals
78(3)
3.5.2 Consonant and Dissonant Intervals and Tone Progression
81(1)
3.5.3 Elementary Counterpoint
82(3)
3.5.4 Chords
85(9)
3.5.5 Modulations
94(1)
3.6 Time Structures of Music
95(5)
3.6.1 Note Values
95(2)
3.6.2 Measure
97(1)
3.6.3 Meter
97(2)
3.6.4 Rhythm
99(1)
3.7 Elementary Theory of Form
100(7)
3.8 Further Reading
107(4)
Bibliography
108(3)
4 Digital Filters and Spectral Analysis
111(34)
4.1 Introduction
111(1)
4.2 Continuous-Time, Discrete-Time, and Digital Signals
111(1)
4.3 Discrete-Time Systems
112(11)
4.3.1 Parametric LTI Systems
116(2)
4.3.2 Digital Filters and Filter Design
118(5)
4.4 Spectral Analysis Using the Discrete Fourier Transform
123(7)
4.4.1 The Discrete Fourier Transform
123(4)
4.4.2 Frequency Resolution and Zero Padding
127(2)
4.4.3 Short-Time Spectral Analysis
129(1)
4.5 The Constant-Q Transform
130(1)
4.6 Filter Banks for Short-Time Spectral Analysis
131(5)
4.6.1 Uniform Filter Banks
132(3)
4.6.2 Nonuniform Filter Banks
135(1)
4.7 The Cepstrum
136(2)
4.8 Fundamental Frequency Estimation
138(2)
4.9 Further Reading
140(5)
Bibliography
141(4)
5 Signal-Level Features
145(20)
5.1 Introduction
145(1)
5.2 Timbre Features
146(7)
5.2.1 Time-Domain Features
146(1)
5.2.2 Frequency-Domain Features
147(4)
5.2.3 Mel Frequency Cepstral Coefficients
151(2)
5.3 Harmony Features
153(4)
5.3.1 Chroma Features
153(1)
5.3.2 Chroma Energy Normalized Statistics
154(1)
5.3.3 Timbre-Invariant Chroma Features
155(1)
5.3.4 Characteristics of Partials
156(1)
5.4 Rhythmic Features
157(5)
5.4.1 Features for Onset Detection
157(2)
5.4.2 Phase-Domain Characteristics
159(1)
5.4.3 Fluctuation Patterns
160(2)
5.5 Further Reading
162(3)
Bibliography
162(3)
6 Auditory Models
165(12)
6.1 Introduction
165(1)
6.2 Auditory Periphery
166(1)
6.3 The Meddis Model of the Auditory Periphery
167(3)
6.3.1 Outer and Middle Ear
168(1)
6.3.2 Basilar Membrane
169(1)
6.3.3 Inner Hair Cells
169(1)
6.3.4 Auditory Nerve Synapse
169(1)
6.3.5 Auditory Nerve Activity
170(1)
6.4 Pitch Estimation Using Auditory Models
170(2)
6.4.1 Autocorrelation Models
170(1)
6.4.2 Pitch Extraction in the Brain
171(1)
6.5 Further Reading
172(5)
Bibliography
173(4)
7 Digital Representation of Music
177(20)
7.1 Introduction
177(1)
7.2 From Sheet to File
178(8)
7.2.1 Optical Music Recognition
178(1)
7.2.2 Abc Music Notation
179(1)
7.2.3 Musical Instrument Digital Interface
180(4)
7.2.4 Music XML 3.0
184(2)
7.3 From Signal to File
186(7)
7.3.1 Pulse Code Modulation and Raw Audio Format
187(2)
7.3.2 WAVE File Format
189(1)
7.3.3 MP3 Compression
190(3)
7.4 From File to Sheet
193(2)
7.4.1 Music TeX Typesetting
194(1)
7.4.2 Transcription Tools
195(1)
7.5 From File to Signal
195(1)
7.6 Further Reading
196(1)
Bibliography
196(1)
8 Music Data: Beyond the Signal Level
197(20)
8.1 Introduction
197(1)
8.2 From the Signal Level to Semantic Features
198(3)
8.2.1 Types of Semantic Features
198(1)
8.2.2 Deriving Semantic Features
199(1)
8.2.3 Discussion
200(1)
8.3 Symbolic Features
201(2)
8.4 Music Scores
203(1)
8.5 Social Web
204(4)
8.5.1 Social Tags
205(1)
8.5.2 Shared Playlists
205(2)
8.5.3 Listening Activity
207(1)
8.6 Music Databases
208(1)
8.7 Lyrics
209(3)
8.8 Concluding Remarks
212(5)
Bibliography
212(5)
II Methods
217(192)
9 Statistical Methods
219(44)
9.1 Introduction
219(1)
9.2 Probability
219(4)
9.2.1 Theory
219(3)
9.2.2 Empirical Analogues
222(1)
9.3 Random Variables
223(4)
9.3.1 Theory
223(2)
9.3.2 Empirical Analogues
225(2)
9.4 Characterization of Random Variables
227(9)
9.4.1 Theory
227(2)
9.4.2 Empirical Analogues
229(4)
9.4.3 Important Univariate Distributions
233(3)
9.5 Random Vectors
236(6)
9.5.1 Theory
236(3)
9.5.2 Empirical Analogues
239(3)
9.6 Estimators of Unknown Parameters and Their Properties
242(2)
9.7 Testing Hypotheses on Unknown Parameters
244(4)
9.8 Modeling of the Relationship between Variables
248(14)
9.8.1 Regression
248(4)
9.8.2 Time Series Models
252(7)
9.8.3 Towards Smaller and Easier to Handle Models
259(3)
9.9 Further Reading
262(1)
Bibliography
262(1)
10 Optimization
263(20)
10.1 Introduction
263(1)
10.2 Basic Concepts
264(2)
10.3 Single-Objective Problems
266(10)
10.3.1 Binary Feasible Sets
266(5)
10.3.2 Continuous Feasible Sets
271(5)
10.3.3 Compound Feasible Sets
276(1)
10.4 Multi-Objective Problems
276(5)
10.5 Further Reading
281(2)
Bibliography
281(2)
11 Unsupervised Learning
283(20)
11.1 Introduction
283(1)
11.2 Distance Measures and Cluster Distinction
284(3)
11.3 Agglomerative Hierarchical Clustering
287(4)
11.3.1 Agglomerative Hierarchical Methods
287(2)
11.3.2 Ward Method
289(1)
11.3.3 Visualization
290(1)
11.4 Partition Methods
291(6)
11.4.1 k-Means Methods
291(2)
11.4.2 Self-Organizing Maps
293(4)
11.5 Clustering Features
297(1)
11.6 Independent Component Analysis
297(4)
11.7 Further Reading
301(2)
Bibliography
302(1)
12 Supervised Classification
303(26)
12.1 Introduction
303(1)
12.2 Supervised Learning and Classification
304(1)
12.3 Targets of Classification
305(1)
12.4 Selected Classification Methods
306(18)
12.4.1 Bayes and Approximate Bayes Methods
307(3)
12.4.2 Nearest Neighbor Prediction
310(2)
12.4.3 Decision Trees
312(2)
12.4.4 Support Vector Machines
314(5)
12.4.5 Ensemble Methods: Bagging
319(1)
12.4.6 Neural Networks
320(4)
12.5 Interpretation of Classification Results
324(1)
12.6 Further Reading
325(4)
Bibliography
326(3)
13 Evaluation
329(36)
13.1 Introduction
329(3)
13.2 Resampling
332(7)
13.2.1 Resampling Methods
334(1)
13.2.2 Hold-Out
334(1)
13.2.3 Cross-Validation
335(1)
13.2.4 Bootstrap
336(2)
13.2.5 Subsampling
338(1)
13.2.6 Properties and Recommendations
338(1)
13.3 Evaluation Measures
339(13)
13.3.1 Loss-Based Performance
339(1)
13.3.2 Confusion Matrix
340(1)
13.3.3 Common Performance Measures Based on the Confusion Matrix
341(2)
13.3.4 Measures for Imbalanced Sets
343(2)
13.3.5 Evaluation of Aggregated Predictions
345(2)
13.3.6 Measures beyond Classification Performance
347(5)
13.4 Hyperparameter Tuning: Nested Resampling
352(2)
13.5 Tests for Comparing Classifiers
354(5)
13.5.1 McNemar Test
354(2)
13.5.2 Pairwise t-Test Based on B Independent Test Data Sets
356(1)
13.5.3 Comparison of Many Classifiers
357(2)
13.6 Multi-Objective Evaluation
359(1)
13.7 Further Reading
360(5)
Bibliography
361(4)
14 Feature Processing
365(24)
14.1 Introduction
365(2)
14.2 Preprocessing
367(6)
14.2.1 Transforms of Feature Domains
367(1)
14.2.2 Normalization
368(3)
14.2.3 Missing Values
371(1)
14.2.4 Harmonization of the Feature Matrix
372(1)
14.3 Processing of Feature Dimension
373(1)
14.4 Processing of Time Dimension
374(6)
14.4.1 Sampling and Order-Independent Statistics
374(1)
14.4.2 Order-Dependent Statistics Based on Time Series Analysis
375(2)
14.4.3 Frame Selection Based on Musical Structure
377(3)
14.5 Automatic Feature Construction
380(3)
14.6 A Note on the Evaluation of Feature Processing
383(2)
14.7 Further Reading
385(4)
Bibliography
385(4)
15 Feature Selection
389(20)
15.1 Introduction
389(1)
15.2 Definitions
390(3)
15.3 The Scope of Feature Selection
393(1)
15.4 Design Steps and Categorization of Methods
394(1)
15.5 Ways to Measure Relevance of Features
395(3)
15.5.1 Correlation-Based Relevance
395(1)
15.5.2 Comparison of Feature Distributions
396(1)
15.5.3 Relevance Derived from Information Theory
397(1)
15.6 Examples for Feature Selection Algorithms
398(4)
15.6.1 Relief
398(2)
15.6.2 Floating Search
400(1)
15.6.3 Evolutionary Search
400(2)
15.7 Multi-Objective Feature Selection
402(2)
15.8 Further Reading
404(5)
Bibliography
405(4)
III Applications
409(198)
16 Segmentation
411(22)
16.1 Introduction
411(1)
16.2 Onset Detection
412(10)
16.2.1 Definition
412(1)
16.2.2 Detection Strategies
413(6)
16.2.3 Goodness of Onset Detection
419(3)
16.3 Tone Phases
422(3)
16.3.1 Reasons for Clustering
422(1)
16.3.2 The Clustering Process
422(3)
16.3.3 Refining the Clustering Process
425(1)
16.4 Musical Structure Analysis
425(3)
16.5 Concluding Remarks
428(1)
16.6 Further Reading
429(4)
Bibliography
430(3)
17 Transcription
433(18)
17.1 Introduction
433(1)
17.2 Data
434(1)
17.3 Musical Challenges: Partials, Vibrato, and Noise
434(1)
17.4 Statistical Challenge: Piecewise Local Stationarity
435(1)
17.5 Transcription Scheme
436(7)
17.5.1 Separation of the Relevant Part of Music
436(1)
17.5.2 Estimation of Fundamental Frequency
436(4)
17.5.3 Classification of Notes, Silence, and Noise
440(2)
17.5.4 Estimation of Relative Length of Notes and Meter
442(1)
17.5.5 Estimation of the Key
443(1)
17.5.6 Final Transcription into Sheet Music
443(1)
17.6 Software
443(1)
17.7 Concluding Remarks
444(1)
17.8 Further Reading
445(6)
Bibliography
446(5)
18 Instrument Recognition
451(18)
18.1 Introduction
451(2)
18.2 Types of Instrument Recognition
453(1)
18.3 Taxonomy Design
454(2)
18.4 Example of Instrument Recognition
456(8)
18.4.1 Labeled Data
456(1)
18.4.2 Taxonomy Design
457(1)
18.4.3 Feature Extraction and Processing
458(1)
18.4.4 Feature Selection and Supervised Classification
459(1)
18.4.5 Evaluation
460(4)
18.4.6 Summary of Example
464(1)
18.5 Concluding Remarks
464(1)
18.6 Further Reading
464(5)
Bibliography
465(4)
19 Chord Recognition
469(24)
19.1 Introduction
469(1)
19.2 Chord Dictionary
470(1)
19.3 Chroma or Pitch Class Profile Extraction
471(5)
19.3.1 Computation Using the Short-Time Fourier Transform
472(1)
19.3.2 Computation Using the Constant-Q Transform
472(2)
19.3.3 Influence of Timbre on the Chroma/PCP
474(2)
19.4 Chord Representation
476(1)
19.4.1 Knowledge-Driven Approach
476(1)
19.4.2 Data-Driven Approach
476(1)
19.5 Frame-Based System for Chord Recognition
477(2)
19.5.1 Knowledge-Driven Approach
477(2)
19.5.2 Data-Driven Approach
479(1)
19.5.3 Chord Fragmentation
479(1)
19.6 Hidden Markov Model-Based System for Chord Recognition
479(4)
19.6.1 Knowledge-Driven Transition Probabilities
481(1)
19.6.2 Data-Driven Transition Probabilities
481(2)
19.7 Joint Chord and Key Recognition
483(2)
19.7.1 Key-Only Recognition
484(1)
19.7.2 Joint Chord and Key Recognition
484(1)
19.8 Evaluating the Performances of Chord and Key Estimation
485(2)
19.8.1 Evaluating Segmentation Quality
485(1)
19.8.2 Evaluating Labeling Quality
485(2)
19.9 Concluding Remarks
487(1)
19.10 Further Reading
487(6)
19.10.1 Alternative Audio Signal Representations
488(1)
19.10.2 Alternative Representations of the Chord Labels
488(1)
19.10.3 Taking into Account Other Musical Concepts
488(1)
Bibliography
489(4)
20 Tempo Estimation
493(18)
20.1 Introduction
493(1)
20.2 Definitions
494(4)
20.2.1 Beat
494(1)
20.2.2 Tempo
495(1)
20.2.3 Metrical Levels
496(1)
20.2.4 Automatic Rhythm Estimation
496(2)
20.3 Overall Scheme of Tempo Estimation
498(3)
20.3.1 Feature List Creation
498(3)
20.3.2 Tempo Induction
501(1)
20.4 Evaluation of Tempo Estimation
501(1)
20.5 A Simple Tempo Estimation System
502(2)
20.6 Applications of Automatic Rhythm Estimation
504(1)
20.7 Concluding Remarks
505(1)
20.8 Further Reading
506(5)
Bibliography
506(5)
21 Emotions
511(30)
21.1 Introduction
511(2)
21.1.1 What Are Emotions?
511(1)
21.1.2 Difference between Basic Emotions, Moods, and Emotional Episodes
512(1)
21.1.3 Personality Differences and Emotion Perception
512(1)
21.2 Theories of Emotions and Models
513(4)
21.2.1 Hevner Clusters of Affective Terms
513(2)
21.2.2 Semantic Differential
515(1)
21.2.3 Schubert Clusters
515(1)
21.2.4 Circumplex Word Mapping by Russell
516(1)
21.2.5 Watson--Tellegen Diagram
516(1)
21.3 Speech and Emotion
517(1)
21.4 Music and Emotion
518(4)
21.4.1 Basic Emotions
518(2)
21.4.2 Moods and Other Affective States
520(2)
21.5 Factors of Influence and Features
522(8)
21.5.1 Harmony and Pitch
522(2)
21.5.2 Melody
524(1)
21.5.3 Instrumentation and Timbre
525(1)
21.5.4 Dynamics
525(1)
21.5.5 Tempo and Rhythm
526(1)
21.5.6 Lyrics, Genres, and Social Data
527(1)
21.5.7 Examples: Individual Comparison of Features
528(2)
21.6 Computationally Based Emotion Recognition
530(4)
21.6.1 A Note on Feature Processing
532(2)
21.6.2 Future Challenges
534(1)
21.7 Concluding Remarks
534(1)
21.8 Further Reading
535(6)
Bibliography
535(6)
22 Similarity-Based Organization of Music Collections
541(22)
22.1 Introduction
541(1)
22.2 Learning a Music Similarity Measure
542(8)
22.2.1 Formalizing an Adaptable Model of Music Similarity
543(1)
22.2.2 Modeling Preferences through Distance Constraints
544(3)
22.2.3 Dealing with Inconsistent Constraint Sets
547(1)
22.2.4 Learning Distance Facet Weights
547(3)
22.3 Visualization: Dealing with Projection Errors
550(5)
22.3.1 Popular Projection Techniques
550(1)
22.3.2 Common and Unavoidable Projection Errors
551(1)
22.3.3 Static Visualization of Local Projection Properties
552(1)
22.3.4 Dynamic Visualization of "Wormholes"
553(2)
22.3.5 Combined Visualization of Different Structural Views
555(1)
22.4 Dealing with Changes in the Collection
555(3)
22.4.1 Incremental Structuring Techniques
556(1)
22.4.2 Aligned Projections
556(2)
22.5 Concluding Remarks
558(1)
22.6 Further Reading
558(5)
Bibliography
559(4)
23 Music Recommendation
563(26)
23.1 Introduction
563(1)
23.2 Common Recommendation Techniques
564(10)
23.2.1 Collaborative Filtering
564(5)
23.2.2 Content-Based Recommendation
569(3)
23.2.3 Further Knowledge Sources and Hybridization
572(2)
23.3 Specific Aspects of Music Recommendation
574(2)
23.4 Evaluating Recommender Systems
576(5)
23.4.1 Laboratory Studies
576(1)
23.4.2 Offline Evaluation and Accuracy Metrics
576(2)
23.4.3 Beyond Accuracy: Additional Quality Factors
578(3)
23.5 Current Topics and Outlook
581(3)
23.5.1 Context-Aware Recommendation
581(1)
23.5.2 Incorporating Social Web Information
582(1)
23.5.3 Playlist Generation
583(1)
23.6 Concluding Remarks
584(1)
23.7 Further Reading
584(5)
Bibliography
585(4)
24 Automatic Composition
589(18)
24.1 Introduction
589(1)
24.2 Composition
589(4)
24.2.1 What Composers Do
589(1)
24.2.2 Why Automatic Composition?
590(2)
24.2.3 A Short History of Automatic Composition
592(1)
24.3 Principles of Automatic Composition
593(10)
24.3.1 Basic Methods
593(6)
24.3.2 Advanced Methods
599(4)
24.3.3 Evaluation of Automatically Composed Music
603(1)
24.4 Concluding Remarks
603(1)
24.5 Further Reading
603(4)
Bibliography
603(4)
IV Implementation
607(58)
25 Implementation Architectures
609(14)
25.1 Introduction
609(1)
25.2 Architecture Variants and Their Evaluation
610(5)
25.2.1 Personal Player Device Processing
612(1)
25.2.2 Network Server-Based Processing
613(1)
25.2.3 Distributed Architectures
614(1)
25.3 Applications
615(2)
25.3.1 Music Recommendation
615(1)
25.3.2 Music Recognition
616(1)
25.4 Novel Applications and Future Development
617(3)
25.5 Concluding Remarks
620(1)
25.6 Further Reading
621(2)
Bibliography
621(2)
26 User Interaction
623(18)
26.1 Introduction
623(2)
26.2 User Input for Music Applications
625(6)
26.2.1 Haptic Input
625(2)
26.2.2 Audio Input
627(2)
26.2.3 Visual and Other Sensor Input
629(1)
26.2.4 Multi-Modal Input
630(1)
26.2.5 Coordination of Inputs from Multiple Users
631(1)
26.3 User Interface Output for Music Applications
631(4)
26.3.1 Audio Presentation
631(1)
26.3.2 Visual Presentation
631(2)
26.3.3 Haptic Presentation
633(1)
26.3.4 Multi-Modal Presentation
634(1)
26.4 Factors Supporting the Interpretation of User Input
635(3)
26.4.1 Role of Context in Music Interaction
635(1)
26.4.2 Impact of Implementation Architectures
636(1)
26.4.3 Influence of Social Interaction and Machine Learning
637(1)
26.5 Concluding Remarks
638(3)
Bibliography
639(2)
27 Hardware Architectures for Music Classification
641(24)
27.1 Introduction
641(1)
27.2 Evaluation Metrics for Hardware Architectures
642(2)
27.2.1 Cost Factors
642(1)
27.2.2 Combined Cost Metrics
643(1)
27.3 Specific Methods for Feature Extraction for Hardware Utilization
644(1)
27.4 Architectures for Digital Signal Processing
644(14)
27.4.1 General Purpose Processor
644(4)
27.4.2 Graphics Processing Unit
648(3)
27.4.3 Digital Signal Processor
651(3)
27.4.4 Application-Specific Instruction Set Processor
654(1)
27.4.5 Dedicated Hardware
654(4)
27.5 Design Space Exploration
658(3)
27.6 Concluding Remarks
661(1)
27.7 Further Reading
662(3)
Bibliography
662(3)
Notation 665(2)
Index 667
Dietmar Jannach, Günter Rudolphm and Igor Vatolkin are affiliated with the Department of Computer Science, TU Dortmund University, Germany









Claus Weihs is affiliated with the Department of Statistics at TU Dortmund University, Germany
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