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Music Data Analysis
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Table of Contents

K25499 TOC Introduction Background and Motivation Content, Target Audience, Prerequisites, Exercises, and Complementary Material Book Overview Chapter Summaries Course Examples Authors and Editors Bibliography I Music and Audio
The Musical Signal - Physically and Psychologically Introduction The Tonal Quality: Pitch - The First Moment Introduction Pure and Complex Tones on a Vibrating String Intervals and Musical Tone Height Musical Notation and Naming of Pitches and Intervals The Mel Scale Fourier Transform Correlation Analysis Fluctuating Pitch and Frequency Modulation Simultaneous Pitches Other Sounds With and Without Pitch Percepts Volume - The Second Moment Introduction The Physical Basis: Sound Waves in Air Scales for the Subjective Perception of the Volume Amplitude Modulation Uncertainty Principle Gabor Transform and Spectrogram Formants, Vowels, and Characteristic Timbres of Voices and Instruments Sound Fluctuations and Timbre Physical Model for the Timbre of Wind Instruments Duration - The Fourth Moment Integration Times and Temporal Resolvability Time Structure in Music: Rhythm and Measure Wavelets and Scalograms Further Reading Exercises Bibliography Musical Structures and Their Perception Introduction Scales and Keys Clefs Diatonic and Chromatic Scales Other Scales Gestalt and Auditory Scene Analysis Musical Textures from Monophony to Polyphony Polyphony and Harmony Dichotomy of Consonant and Dissonant Intervals Consonant and Dissonant Intervals and Tone Progression Elementary Counterpoint Chords Modulations Time Structures of Music Note Values Measure Meter Rhythm Elementary Theory of Form Further Reading Bibliography Digital Filters and Spectral Analysis Introduction Continuous-Time, Discrete-Time, and Digital Signals Discrete-Time Systems Parametric LTI Systems Digital Filters and Filter Design The Discrete Fourier Transform The Discrete Fourier Transform Frequency Resolution and Zero Padding Short-time Spectral Analysis The Constant-Q Transform Filter Banks for Short-time Spectral Analysis Uniform Filter Banks Nonuniform Filter Banks The Cepstrum Fundamental Frequency Estimation Further Reading Bibliography Signal-Level Features Introduction Timbre Features Time-Domain Features Frequency-Domain Features Mel Frequency Cepstral Coefficients Harmony Features Chroma Features Chroma Energy Normalized Statistics Timbre-Invariant Chroma Features Characteristics of Partials Rhythmic Features Features for Onset Detection Phase-Domain Characteristics Fluctuation Patterns Further Reading Bibliography Auditory Models Introduction Auditory Periphery The Meddis Model of the Auditory Periphery Outer and Middle Ear Basilar Membrane Inner Hair Cells Auditory Nerve Synapse Auditory Nerve Activity Pitch Estimation Using Auditory Models Autocorrelation Models Pitch Extraction in the Brain Further Reading Bibliography Digital Representation of Music Introduction From Sheet to File Optical Music Recognition abc Music Notation Musical Instrument Digital Interface MusicXML 3.0 From Signal to File Pulse Code Modulation and Raw Audio Format WAVE File Format MP3 Compression From File to Sheet MusicTeX Typesetting Transcription Tools From File to Signal Further Reading Bibliography Music Data: Beyond the Signal Level Introduction From the Signal Level to Semantic Features Types of Semantic Features Deriving Semantic Features Discussion Symbolic Features Music Scores Social Web Social Tags Shared Playlists Listening Activity Music Databases Concluding Remarks Bibliography II Methods
Statistical Methods Introduction Probability Theory Empirical Analogues Random Variables Theory Empirical Analogues Characterization of Random Variables Theory Empirical Analogues Important Univariate Distributions Random Vectors Theory Empirical Analogues Estimators of Unknown Parameters and their Properties Testing Hypotheses on Unknown Parameters Modeling of the Relationship between Variables Regression Time Series Models Towards Smaller and Easier to Handle Models Further Reading Bibliography Optimization Introduction Basic Concepts Single-Objective Problems Binary Feasible Sets Continuous Feasible Sets Compound Feasible Sets Multi-Objective Problems Further Reading Bibliography Unsupervised Learning Introduction Distance Measures and Cluster Distinction Agglomerative Hierarchical Clustering Agglomerative Hierarchical Methods Ward Method Visualization Partition Methods k-Means Methods Self-Organizing Maps Clustering Features Independent Component Analysis Further Reading Bibliography Supervised Classification Introduction Supervised Learning and Classification Targets of Classification Selected Classification Methods Bayes and Approximate Bayes Methods Nearest Neighbor Prediction Decision Trees Support Vector Machines Ensemble Methods: Bagging Neural Networks Interpretation of Classification Results Further Reading Bibliography Evaluation Introduction Resampling Resampling Methods Hold-Out Cross-Validation Bootstrap Subsampling Properties and Recommendations Evaluation Measures Loss Based Performance Confusion Matrix Common Performance Measures Based on the Confusion Matrix Measures for Imbalanced Sets Evaluation of Aggregated Predictions Measures Beyond Classification Performance Hyperparameter Tuning: Nested Resampling Tests for Comparing Classifiers McNemar Test Pairwise t-Test Based on B Independent Test Data Sets Comparison of Many Classifiers Multi-Objective Evaluation Further Reading Bibliography Feature Processing Introduction Preprocessing Transforms of Feature Domains Normalization Missing Values Harmonization of the Feature Matrix Processing of Feature Dimension Processing of Time Dimension Sampling and Order-Independent Statistics Order-Dependent Statistics Based on Time Series Analysis Frame Selection Based on Musical Structure Automatic Feature Construction A Note on the Evaluation of Feature Processing Further Reading Bibliography Feature Selection Introduction Definitions The Scope of Feature Selection Design Steps and Categorization of Methods Ways to Measure Relevance of Features Correlation-Based Relevance Comparison of Feature Distributions Relevance Derived from Information Theory Examples for Feature Selection Algorithms Relief Floating Search Evolutionary Search Multi-Objective Feature Selection Further Reading Bibliography III Applications
Segmentation Introduction Onset Detection Definition Detection Strategies Goodness of Onset Detection Tone phases Reasons for Clustering The Clustering Process Refining the Clustering Process Musical Structure Analysis Concluding Remarks Further Reading Bibliography Transcription Introduction Data Musical Challenges: Partials, Vibrato, and Noise Statistical Challenge: Piecewise Local Stationarity Transcription Scheme Separation of the Relevant Part of Music Estimation of Fundamental Frequency Classification of Notes, Silence, and Noise Estimation of Relative Length of Notes and Meter Estimation of the Key Final Transcription into Sheet Music Software Concluding Remarks Further Reading Bibliography Instrument Recognition Introduction Types of Instrument Recognition Taxonomy Design Example of Instrument Recognition Labeled Data Taxonomy Design Feature Extraction and Processing Feature Selection and Supervised Classification Evaluation Summary of Example Concluding Remarks Further Reading Bibliography Chord Recognition Introduction Chord Dictionary Chroma or Pitch Class Profile Extraction Computation Using the Short-Time-Fourier-Transform Computation Using the Constant-Q-Transform Influence of Timbre on the Chroma/PCP Chord Representation Knowledge-driven Approach Data-driven Approach Frame-based System for Chord Recognition Knowledge-driven Approach Data-driven Approach Chord Fragmentation Hidden Markov Model-based System for Chord Recognition Knowledge-driven Transition Probabilities Data-driven Transition Probabilities Joint Chord and Key Recognition Key-Only Recognition Joint Chord and Key Recognition Evaluating the Performances of Chord and Key Estimation Evaluating Segmentation Quality Evaluating Labeling Quality Concluding Remarks Further Reading Alternative Audio Signal Representations Alternative Representations of the Chord Labels Taking into Account other Musical Concepts Bibliography Tempo Estimation Introduction Definitions Beat Tempo Metrical Levels Automatic Rhythm Estimation Overall Scheme of Tempo Estimation Feature List Creation Tempo Induction Evaluation of Tempo Estimation A Simple Tempo Estimation System Applications of Automatic Rhythm Estimation Concluding Remarks Further Reading Bibliography Emotions Introduction What are Emotions? Difference between Basic Emotions, Moods, and Emotional Episodes Personality Differences and Emotion Perception Theories of Emotions and Models Hevner Clusters of Affective Terms Semantic Differential Schubert Clusters Circumplex Word Mapping by Russell Watson-Tellegen Diagram Speech and Emotion Music and Emotion Basic Emotions Moods and Other Affective States Factors of Influence and Features Harmony and Pitch Melody Instrumentation and Timbre Dynamics Tempo and Rhythm Lyrics, Genres, and Social Data Examples: Individual Comparison of Features Computationally Based Emotion Recognition A Note on Feature Processing Future Challenges Concluding Remarks Further Reading Bibliography Similarity-based Organization of Music Collections Introduction Learning a Music Similarity Measure Formalizing an Adaptable Model of Music Similarity Modeling Preferences through Distance Constraints Dealing with Inconsistent Constraint Sets Learning Distance Facet Weights Visualization: Dealing with Projection Errors Popular Projection Techniques Common and Unavoidable Projection Errors Static Visualization of Local Projection Properties Dynamic Visualization of "Wormholes" Combined Visualization of Different Structural Views Dealing with Changes in the Collection Incremental Structuring Techniques Aligned Projections Concluding Remarks Further Reading Bibliography Music Recommendation Introduction Common Recommendation Techniques Collaborative Filtering Content-based Recommendation Further Knowledge Sources and Hybridization Specific Aspects of Music Recommendation Evaluating Recommender Systems Laboratory Studies Offline Evaluation and Accuracy Metrics Beyond Accuracy - Additional Quality Factors Current Topics and Outlook Context-Aware Recommendation Incorporating Social Web information Playlist Generation Concluding Remarks Further Reading Bibliography Automatic Composition Introduction Composition What Composers Do Why Automatic Composition? A Short History of Automatic Composition Principles of Automatic Composition Basic Methods Advanced Methods Evaluation of Automatically Composed Music Concluding Remarks Further Reading Bibliography IV Implementation
Implementation Architectures Introduction Architecture Variants and their Evaluation Personal Player Device Processing Network Server Based Processing Distributed Architectures Applications Music Recommendation Music Recognition Novel Applications and Future Development Concluding Remarks Further Reading Bibliography User Interaction Introduction User Input for Music Applications Haptic Input Audio Input Visual and Other Sensor Input Multi-Modal Input Coordination of Inputs from Multiple Users User Interface Output for Music Applications Audio Presentation Visual Presentation Haptic Presentation Multi-Modal Presentation Factors Supporting the Interpretation of User Input Role of Context in Music Interaction Impact of Implementation Architectures Influence of Social Interaction and Machine Learning Concluding Remarks Bibliography Hardware Architectures for Music Classification Introduction Evaluation Metrics for Hardware Architectures Cost Factors Combined Cost Metrics Specific Methods for Feature Extraction for Hardware Utilization Architectures for Digital Signal Processing General Purpose Processor Graphics Processing Unit Digital Signal Processor Application Specific Instruction Set Processor Dedicated Hardware Design Space Exploration Concluding Remarks Further Reading Bibliography Index

About the Author

Dietmar Jannach, Gunter 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

Reviews

" . . . 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 book's 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

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