A color set web-based agent system for 2-dimension emotion image space
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Exploiting online music tags for music emotion classification
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Machine Recognition of Music Emotion: A Review
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Music retagging using label propagation and robust principal component analysis
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Supervised dictionary learning for music genre classification
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Inferring personal traits from music listening history
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Exploring the relationship between categorical and dimensional emotion semantics of music
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The acoustic emotion gaussians model for emotion-based music annotation and retrieval
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On sparse and low-rank matrix decomposition for singing voice separation
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Bilingual analysis of song lyrics and audio words
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The acousticvisual emotion guassians model for automatic generation of music video
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Parametrisation and correlation analysis applied to music mood classification
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1000 songs for emotional analysis of music
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Providing acomplete 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 comprehensiveintroduction to the essential aspects of MERincluding 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 psychologistsvalence 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.