Music Data Analysis: Foundations and Applications, 1st Edition (Hardback) book cover

Music Data Analysis

Foundations and Applications, 1st Edition

Edited by Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph

Chapman and Hall/CRC

676 pages | 259 B/W Illus.

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Description

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.

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

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 Editors

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

About the Series

Chapman & Hall/CRC Computer Science & Data Analysis

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM021030
COMPUTERS / Database Management / Data Mining
MAT029000
MATHEMATICS / Probability & Statistics / General
SCI010000
SCIENCE / Biotechnology