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Music Emotion Recognition




ISBN 9781439850466
Published February 22, 2011 by CRC Press
261 Pages - 74 B/W Illustrations

 
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Book Description

Providing a complete review of existing work in music emotion developed in psychology and engineering, Music Emotion Recognition explains how to account for the subjective nature of emotion perception in the development of automatic music emotion recognition (MER) systems. Among the first publications dedicated to automatic MER, it begins with a comprehensive introduction to the essential aspects of MER—including background, key techniques, and applications.

This ground-breaking reference examines emotion from a dimensional perspective. It defines emotions in music as points in a 2D plane in terms of two of the most fundamental emotion dimensions according to psychologists—valence and arousal. The authors present a computational framework that generalizes emotion recognition from the categorical domain to real-valued 2D space. They also:

  • Introduce novel emotion-based music retrieval and organization methods
  • Describe a ranking-base emotion annotation and model training method
  • Present methods that integrate information extracted from lyrics, chord sequence, and genre metadata for improved accuracy
  • Consider an emotion-based music retrieval system that is particularly useful for mobile devices

The book details techniques for addressing the issues related to: the ambiguity and granularity of emotion description, heavy cognitive load of emotion annotation, subjectivity of emotion perception, and the semantic gap between low-level audio signal and high-level emotion perception. Complete with more than 360 useful references, 12 example MATLAB® codes, and a listing of key abbreviations and acronyms, this cutting-edge guide supplies the technical understanding and tools needed to develop your own automatic MER system based on the automatic recognition model.

Table of Contents

Introduction
Importance of Music Emotion Recognition
Recognizing the Perceived Emotion of Music
Issues of Music Emotion Recognition
     Ambiguity and Granularity of Emotion Description 
     Heavy Cognitive Load of Emotion Annotation
     Subjectivity of Emotional Perception 
     Semantic Gap between Low-Level Audio Signal and High-Level Human Perception

Overview of Emotion Description and Recognition
Emotion Description
     Categorical Approach 
     Dimensional Approach
     Music Emotion Variation Detection
Emotion Recognition
     Categorical Approach 
     Dimensional Approach
     Music Emotion Variation Detection

Music Features
Energy Features
Rhythm Features
Temporal Features
Spectrum Features
Harmony Features

Dimensional MER by Regression
Adopting the Dimensional Conceptualization of Emotion
VA Prediction 
     Weighted-Sum of Component Functions 
     Fuzzy Approach 
     System Identification Approach (System ID)
The Regression Approach
     Regression Theory 
     Problem Formulation 
     Regression Algorithms
System Overview
Implementation 
     Data Collection 
     Feature Extraction 
     Subjective Test
     Regressor Training
Performance Evaluation 
     Consistency Evaluation of the Ground Truth 
     Data Transformation 
     Feature Selection
     Accuracy of Emotion Recognition 
     Performance Evaluation for Music Emotion Variation Detection  
     Performance Evaluation for Emotion Classification

Ranking-Based Emotion Annotation and Model Training
Motivation
Ranking-Based Emotion Annotation
Computational Model for Ranking Music by Emotion 
     Learning-to-Rank 
     Ranking Algorithms
System Overview
 Implementation 
     Data Collection 
     Feature Extraction
Performance Evaluation 
     Cognitive Load of Annotation 
     Accuracy of Emotion Recognition
     Subjective Evaluation of the Prediction Result

Fuzzy Classification of Music Emotion 
Motivation
Fuzzy Classification
     Fuzzy k-NN Classifier 
     Fuzzy Nearest-Mean Classifier
System Overview
Implementation 
     Data Collection 
     Feature Extraction and Feature Selection
Performance Evaluation 
     Accuracy of Emotion Classification
     Music Emotion Variation Detection

Personalized MER and Groupwise MER
Motivation
Personalized MER
Groupwise MER
Implementation 
     Data Collection 
     Personal Information Collection 
     Feature Extraction
Performance Evaluation 
     Performance of the General Method
     Performance of GWMER
     Performance of PMER

Two-Layer Personalization
Problem Formulation
Bag-of-Users Model
Residual Modeling and Two-Layer Personalization Scheme
Performance Evaluation

Probability Music Emotion Distribution Prediction
Motivation
Problem Formulation
The KDE-Based Approach to Music Emotion Distribution Prediction 
     Ground Truth Collection 
      Regressor Training
     Regressor Fusion
     Output of Emotion Distribution
Implementation 
     Data Collection 
     Feature Extraction
Performance Evaluation 
     Comparison of Different Regression Algorithms
     Comparison of Different Distribution Modeling Methods
     Comparison of Different Feature Representations 
     Evaluation of Regressor Fusion

Lyrics Analysis and Its Application to MER
Motivation
Lyrics Feature Extraction
     Uni-gram 
     Probabilistic Latent Semantic Analysis (PLSA) 
     Bi-gram 
Multimodal MER System
Performance Evaluation
     Comparison of Multimodal Fusion Methods
     Evaluation for PLSA Model
     Evaluation for Bi-Gram Model

Chord Recognition and Its Application to MER
Chord Recognition 
     Beat Tracking and PCP Extraction 
     Hidden Markov Model and N-Gram Model
     Chord Decoding 
     Chord Features
     Longest Common Chord Subsequence
     Chord Histogram
System Overview
Performance Evaluation 
     Evaluation of Chord Recognition System 
     Accuracy of Emotion Classification

Genre Classification and Its Application to MER
Motivation
Two-Layer Music Emotion Classification
Performance Evaluation 
     Data Collection 
     Analysis of the Correlation between Genre and Emotion
     Evaluation of the Two-Layer Emotion Classification Scheme

Music Retrieval in the Emotion Plane
Emotion-Based Music Retrieval
2D Visualization of Music
Retrieval Methods 
     Query by Emotion Point (QBEP) 
     Query by Emotion Trajectory (QBET) 
     Query by Artist and Emotion (QBAE) 
     Query by Lyrics and Emotion (QBLE)
Implementation

Future Research Directions
Exploiting Vocal Timbre for MER
Emotion Distribution Prediction Based on Rankings
Personalized Emotion-Based Music Retrieval 
Situational Factors of Emotion Perception 
Connections between Dimensional and Categorical MER
Music Retrieval and Organization in 3D Emotion Space

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Author(s)

Biography

Yi-Hsuan Yang received a Ph.D. in Communication Engineering from National Taiwan University in 2010. His research interests include multimedia information retrieval, music analysis, machine learning, and affective computing. He has published over 30 technical papers in the above areas. Dr. Yang was awarded MediaTek Fellowship in 2009 and Microsoft Research Asia Fellowship in 2008.

Homer H. Chen received a Ph.D. in Electrical and Computer Engineering from the University of Illinois at Urbana- Champaign. Since August 2003, he has been with the College of Electrical Engineering and Computer Science, National Taiwan University, where he is Irving T. Ho Chair Professor. Prior to that, he held various R&D management and engineering positions with US companies over a period of 17 years, including AT&T Bell Labs, Rockwell Science Center, iVast, and Digital Island. He was a US delegate for ISO and ITU standards committees and contributed to the development of many new interactive multimedia technologies that are now part of the MPEG-4 and JPEG-2000 standards. His professional interests lie in the broad area of multimedia signal processing and communications.

Dr. Chen is an Associate Editor of IEEE Transactions on Circuits and Systems for Video Technology. He served as Associate Editor of IEEE Transactions on Image Processing from 1992 to 1994, Guest Editor of IEEE Transactions on Circuits and Systems for Video Technology in 1999, and an Associate Editorial of Pattern Recognition from 1989 to 1999. He is an IEEE Fellow.