Yang / Chen Music Emotion Recognition

Erscheinungsjahr 2011
ISBN: 978-1-4398-5047-3
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 261 Seiten

Reihe: Multimedia Computing, Communication and Intelligence

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.

Zielgruppe

Researchers and students in computer science, engineering, psychology, and musicology; and industrial practitioners in mobile multimedia, database management, digital home, computer-human interaction, and music information retrieval.

Autoren/Hrsg.

Yang, Yi-Hsuan

Chen, Homer H.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Tonsignalverarbeitung
• Sozialwissenschaften Psychologie Allgemeine Psychologie Kognitionspsychologie
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

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

Über Autor(innen)

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.