Music data mining 1st Edition by Tao Li, George Tzanetakis, Mitsunori Ogihara ISBN 9781439835524 by Li, Tao(editor);ogihara, Mitsunori(editor);tzanetakis, George(editor) 9781439835524, 9781439835555, 9781711711720, 1439835527, 1439835551, 1711711721 instant download after payment.

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ISBN 13: 9781439835524
Author: Tao Li, George Tzanetakis, Mitsunori Ogihara

The research area of music information retrieval has gradually evolved to address the challenges of effectively accessing and interacting large collections of music and associated data, such as styles, artists, lyrics, and reviews. Bringing together an interdisciplinary array of top researchers, Music Data Mining presents a variety of approaches to successfully employ data mining techniques for the purpose of music processing. The book first covers music data mining tasks and algorithms and audio feature extraction, providing a framework for subsequent chapters. With a focus on data classification, it then describes a computational approach inspired by human auditory perception and examines instrument recognition, the effects of music on moods and emotions, and the connections between power laws and music aesthetics. Given the importance of social aspects in understanding music, the text addresses the use of the Web and peer-to-peer networks for both music data mining and evaluating music mining tasks and algorithms. It also discusses indexing with tags and explains how data can be collected using online human computation games. The final chapters offer a balanced exploration of hit song science as well as a look at symbolic musicology and data mining. The multifaceted nature of music information often requires algorithms and systems using sophisticated signal processing and machine learning techniques to better extract useful information. An excellent introduction to the field, this volume presents state-of-the-art techniques in music data mining and information retrieval to create novel ways of interacting with large music collections.

Music data mining 1st Table of contents:

v1 Music Data Mining: An Introduction
1.1 Music Data Sources
1.2 An Introduction to Data Mining
1.2.1 Data Management
1.2.2 Data Preprocessing
1.2.3 Data Mining Tasks and Algorithms
1.2.3.1 Data Visualization
1.2.3.2 Association Mining
1.2.3.3 Sequence Mining
1.2.3.4 Classification
1.2.3.5 Clustering
1.2.3.6 Similarity Search
1.3 Music Data Mining
1.3.1 Overview
1.3.2 Music Data Management
1.3.3 Music Visualization
1.3.4 Music Information Retrieval
1.3.5 Association Mining
1.3.6 Sequence Mining
1.3.7 Classification
1.3.8 Clustering
1.3.9 Music Summarization
1.3.10 Advanced Music Data Mining Tasks
1.4 Conclusion
Methodology issues:
Bibliography
2 Audio Feature Extraction
2.1 Audio Representations
2.1.1 The Short-Time Fourier Transform
2.1.2 Filterbanks, Wavelets, and Other Time-Frequency Representations
2.2 Timbral Texture Features
2.2.1 Spectral Features
2.2.2 Mel-Frequency Cepstral Coefficients
2.2.3 Other Timbral Features
2.2.4 Temporal Summarization
2.2.5 Song-Level Modeling
2.3 Rhythm Features
2.3.1 Onset Strength Signal
2.3.2 Tempo Induction and Beat Tracking
2.3.3 Rhythm Representations
2.4 Pitch/Harmony Features
2.5 Other Audio Features
2.6 Musical Genre Classification of Audio Signals
2.7 Software Resources
2.8 Conclusion
Bibliography
II Classification
3 Auditory S parse Coding
3.1 Introduction
3.1.1 The Stabilized Auditory Image
3.2 Algorithm
3.2.1 Pole—Zero Filter Cascade
3.2.2 Image Stabilization
3.2.3 Box Cutting
3.2.4 Vector Quantization
3.2.5 Machine Learning
3.3 Experiments
3.3.1 Sound Ranking
3.3.2 MIREX 2010
3.4 Conclusion
Bibliography
4 Instrument Recognition
4.1 Introduction
4.2 Scope Delimitation
4.2.1 Pitched and Unpitched Instruments
4.2.2 Signal Complexity
4.2.3 Number of Instruments
4.3 Problem Basics
4.3.1 Signal Segmentation
4.3.2 Feature Extraction
4.3.3 Classification Procedure
4.3.3.1 Classification Systems
4.3.3.2 Hierarchical and Flat Classifications
4.3.4 Analysis and Presentation of Results
4.4 Proposed Solutions
4.4.1 Monophonic Case
4.4.2 Polyphonic Case
4.4.3 Other Relevant Work
4.5 Future Directions
Bibliography
5 Mood and Emotional Classification
5.1 Using Emotions and Moods for Music Retrieval
5.2 Emotion and Mood: Taxonomies, Communication, and Induction
5.2.1 What Is Emotion, What Is Mood?
5.2.2 A Hierarchical Model of Emotions
5.2.3 Labeling Emotion and Mood with Words and Its Issues
5.2.4 Adjective Grouping and the Hevner Diagram
5.2.5 Multidimensional Organizations of Emotion
5.2.5.1 Three and Higher Dimensional Diagrams
5.2.6 Communication and Induction of Emotion and Mood
5.3 Obtaining Emotion and Mood Labels
5.3.1 A Small Number of Human Labelers
5.3.2 A Large Number of Labelers
5.3.3 Mood Labels Obtained from Community Tags
5.3.3.1 MIREX Mood Classification Data
5.3.3.2 Latent Semantic Analysis on Mood Tags
5.3.3.3 Screening by Professional Musicians
5.4 Examples of Music Mood and Emotion Classification
5.4.1 Mood Classfication Using Acoustic Data Analysis
5.4.2 Mood Classification Based on Lyrics
5.4.3 Mixing Audio and Tag Features for Mood Classification
5.4.4 Mixing Audio and Lyrics for Mood Classification
5.4.4.1 Further Exploratory Investigations with More Complex Feature Sets
5.4.5 Exploration of Acoustic Cues Related to Emotions
5.4.6 Prediction of Emotion Model Parameters
5.5 Discussion
Bibliography
6 Zipf’s Law, P ower Laws, and Music Aesthetics
6.1 Introduction
6.1.1 Overview
6.2 Music Information Retrieval
6.2.1 Genre and Author Classification
6.2.1.1 Audio Features
6.2.1.2 MIDI Features
6.2.2 Other Aesthetic Music Classification Tasks
6.3 Quantifying Aesthetics
6.4 Zipf ’s Law and Power Laws
6.4.1 Zipf’s Law
6.4.2 Music and Zipf ’s Law
6.5 Power-Law Metrics
6.5.1 Symbolic (MIDI) Metrics
6.5.1.1 Regular Metrics
6.5.1.2 Higher-Order Metrics
6.5.1.3 Local Variability Metrics
6.5.2 Timbre (Audio) Metrics
6.5.2.1 Frequency Metric
6.5.2.2 Signal Higher-Order Metrics
6.5.2.3 Intrafrequency Higher-Order Metrics
6.5.2.4 Interfrequency Higher-Order Metrics
Discussion
6.6 Automated Classification Tasks
6.6.1 Popularity Prediction Experiment
6.6.1.1 ANN Classification
6.6.2 Style Classification Experiments
6.6.2.1 Multiclass Classification
6.6.2.2 Multiclass Classification (Equal Class Sizes)
6.6.2.3 Binary-Class Classification (Equal Class Sizes)
6.6.3 Visualization Experiment
6.6.3.1 Self-Organizing Maps
Discussion
6.7 Armonique—A Music Similarity Engine
6.8 Psychological Experiments
6.8.1 Earlier Assessment and Validation
6.8.1.1 Artificial Neural Network Experiment
6.8.1.2 Evolutionary Computation Experiment
6.8.1.3 Music Information Retrieval Experiment
6.8.2 Armonique Evaluation Experiments
6.8.2.1 Methodology
6.8.2.2 Results—Psychological Ratings
6.8.2.3 Results—Physiological Measures
6.8.2.4 Discussion
6.8.2.5 Final Thoughts
6.9 Conclusion
Acknowledgments
Bibliography
III Social Aspects of Music Data Mining
7 Web-Based and Community-Based Music Information Extraction
7.1 Approaches to Extract Information about Music
7.1.1 Song Lyrics
7.1.2 Country of Origin
7.1.3 Band Members and Instrumentation
7.1.4 Album Cover Artwork
7.2 Approaches to Similarity Measurement
7.2.1 Text-Based Approaches
7.2.1.1 Term Profiles from Web Pages
7.2.1.2 Collaborative Tags
7.2.1.3 Song Lyrics
7.2.2 Co-Occurrence—Based Approaches
7.2.2.1 Web-Based Co-Occurrences and Page Counts
7.2.2.2 Playlists
7.2.2.3 Peer-to-Peer Networks
7.3 Conclusion
Acknowledgments
Bibliography
8 Indexing Music with Tags
8.1 Introduction
8.2 Music Indexing
8.2.1 Indexing Text
8.2.2 Indexing Music
8.3 Sources of Tag-Based Music Information
8.3.1 Conducting a Survey
8.3.2 Harvesting Social Tags
8.3.3 Playing Annotation Games
8.3.4 Mining Web Documents
8.3.5 Autotagging Audio Content
8.3.6 Additional Remarks
8.4 Comparing Sources of Music Information
8.4.1 Social Tags: Last.fm
8.4.2 Games: ListenGame
8.4.3 Web Documents: Weight-Based Relevance Scoring
8.4.4 Autotagging: Supervised Multiclass Labeling
8.4.5 Summary
8.5 Combining Sources of Music Information
8.5.1 Ad-Hoc Combination Approaches
8.5.2 Learned Combination Approaches
8.5.3 Comparison
8.6 Meerkat: A Semantic Music Discovery Engine
Glossary
Acknowledgments
Bibliography
9 Human C omputation for Music Classification
9.1 Introduction
9.2 TagAtune: A Music Tagging Game
9.2.1 Input-Agreement Mechanism
9.2.2 Fun Game, Noisy Data
9.2.3 A Platform for Collecting Human Evaluation
9.2.3.1 The TagATune Metric
9.2.3.2 MIREX Special TagATune Evaluation
Algorithm Ranking
Game Statistics
9.2.3.3 Strength and Weaknesses
9.3 Learning to Tag Using Tagatune Data
9.3.1 A Brief Introduction to Topic Models
9.3.2 Leveraging Topic Models for Music Tagging
9.3.2.1 Experimental Results Feasibility
Annotation and Retrieval Performance
Efficiency
Human Evaluation
9.4 Conclusion
Acknowledgments
Bibliography
IV Advanced Topics
10 Hit Song S cience
10.1 An Inextricable Maze?
10.1.1 Music Psychology and the Exposure Effect
10.1.2 The Broadcaster/Listener Entanglement
10.1.3 Social Influence
10.1.4 Modeling the Life Span of Hits
10.2 In Search of the Features of Popularity
10.2.1 Features: The Case of Birds
10.2.2 The Ground-Truth Issue
10.2.3 Audio and Lyrics Features: The Initial Claim
10.3 A Large-Scale Study
10.3.1 Generic Audio Features
10.3.2 Specific Audio Features
10.3.3 Human Features
10.3.4 The HiFind Database
10.3.4.1 A Controlled Categorization Process
10.3.4.2 Assessing Classifiers
10.3.5 Experiment
10.3.5.1 Design
10.3.5.2 Random Oracles
10.3.5.3 Evaluation of Acoustic Classifiers
10.3.5.4 Inference from Human Data
10.3.6 Summary
10.4 Discussion
Bibliography
11 Symbolic D ata Mining in Musicology
11.1 Introduction
11.2 The Role of the Computer
11.3 Symbolic Data Mining Methodology
11.3.1 Defining the Problem
11.3.2 Encoding and Normalization
11.3.3 Musicological Interpretation
11.4 Case Study: the Buxheim Organ Book
11.4.1 Research Questions
11.4.2 Encoding and Normalization
11.4.3 Extraction, Filtering, and Interpretation
11.4.3.1 Double Leading Tones
11.4.3.2 Keyboard Tuning
11.5 Conclusion
Bibliography

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Tags: Tao Li, George Tzanetakis, Mitsunori Ogihara, Music, mining