Description
Taylor & Francis Ltd Music Emotion Recognition 2011 Edition 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 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 (R) 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. IntroductionImportance of Music Emotion Recognition Recognizing the Perceived Emotion of MusicIssues of Music Emotion Recognition Ambiguity and Granularity of Emotion Description Heavy Cognitive Load of Emotion AnnotationSubjectivity of Emotional Perception Semantic Gap between Low-Level Audio Signal and High-Level Human PerceptionOverview of Emotion Description and RecognitionEmotion DescriptionCategorical Approach Dimensional ApproachMusic Emotion Variation Detection Emotion RecognitionCategorical Approach Dimensional ApproachMusic Emotion Variation DetectionMusic Features Energy FeaturesRhythm FeaturesTemporal Features Spectrum FeaturesHarmony FeaturesDimensional MER by Regression Adopting the Dimensional Conceptualization of EmotionVA Prediction Weighted-Sum of Component Functions Fuzzy Approach System Identification Approach (System ID) The Regression ApproachRegression Theory Problem Formulation Regression Algorithms System OverviewImplementation Data Collection Feature Extraction Subjective TestRegressor TrainingPerformance Evaluation Consistency Evaluation of the Ground Truth Data Transformation Feature SelectionAccuracy of Emotion Recognition Performance Evaluation for Music Emotion Variation Detection Performance Evaluation for Emotion ClassificationRanking-Based Emotion Annotation and Model Training Motivation Ranking-Based Emotion AnnotationComputational Model for Ranking Music by Emotion Learning-to-Rank Ranking AlgorithmsSystem OverviewImplementation Data Collection Feature Extraction Performance Evaluation Cognitive Load of Annotation Accuracy of Emotion RecognitionSubjective Evaluation of the Prediction ResultFuzzy Classification of Music Emotion Motivation Fuzzy ClassificationFuzzy k-NN Classifier Fuzzy Nearest-Mean Classifier System OverviewImplementation Data Collection Feature Extraction and Feature Selection Performance Evaluation Accuracy of Emotion ClassificationMusic Emotion Variation DetectionPersonalized MER and Groupwise MERMotivation Personalized MERGroupwise MERImplementation Data Collection Personal Information Collection Feature Extraction Performance Evaluation Performance of the General MethodPerformance of GWMERPerformance of PMERTwo-Layer Personalization Problem FormulationBag-of-Users Model Residual Modeling and Two-Layer Personalization Scheme Performance EvaluationProbability Music Emotion Distribution Prediction Motivation Problem FormulationThe KDE-Based Approach to Music Emotion Distribution Prediction Ground Truth Collection Regressor TrainingRegressor FusionOutput of Emotion DistributionImplementation Data Collection Feature Extraction Performance Evaluation Comparison of Different Regression AlgorithmsComparison of Different Distribution Modeling MethodsComparison of Different Feature Representations Evaluation of Regressor FusionLyrics Analysis and Its Application to MER Motivation Lyrics Feature ExtractionUni-gram Probabilistic Latent Semantic Analysis (PLSA) Bi-gram Multimodal MER System Performance Evaluation Comparison of Multimodal Fusion MethodsEvaluation for PLSA ModelEvaluation for Bi-Gram ModelChord Recognition and Its Application to MERChord Recognition Beat Tracking and PCP Extraction Hidden Markov Model and N-Gram Model Chord Decoding Chord FeaturesLongest Common Chord SubsequenceChord Histogram System OverviewPerformance Evaluation Evaluation of Chord Recognition System Accuracy of Emotion ClassificationGenre Classification and Its Application to MERMotivation Two-Layer Music Emotion Classification Performance Evaluation Data Collection Analysis of the Correlation between Genre and EmotionEvaluation of the Two-Layer Emotion Classification SchemeMusic Retrieval in the Emotion Plane Emotion-Based Music Retrieval 2D Visualization of MusicRetrieval Methods Query by Emotion Point (QBEP) Query by Emotion Trajectory (QBET) Query by Artist and Emotion (QBAE) Query by Lyrics and Emotion (QBLE)ImplementationFuture Research DirectionsExploiting Vocal Timbre for MEREmotion Distribution Prediction Based on Rankings Personalized Emotion-Based Music Retrieval Situational Factors of Emotion Perception Connections between Dimensional and Categorical MERMusic Retrieval and Organization in 3D Emotion Space