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    978-1-43-985046-6
    February 21st 2011

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.

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

Author Bio

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.

Name: Music Emotion Recognition (Hardback)CRC Press 
Description: By Yi-Hsuan Yang, Homer H. Chen. 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...
Categories: Databases, Internet & Multimedia, Data Preparation & Mining