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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 MERincluding background, key techniques, and applications.This groundbreaking 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 realvalued 2D space. They also:Introduce novel emotionbased music retrieval and organization methods Describe a rankingbase emotion annotation and model training method Present methods that integrate information extracted from lyrics, chord sequence, and genre metadata for improved accuracy Consider an emotionbased music retrieval system that is particularly useful for mobile devicesThe 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 lowlevel audio signal and highlevel emotion perception. Complete with more than 360 useful references, 12 example MATLAB codes, and a listing of key abbreviations and acronyms, this cuttingedge guide supplies the technical understanding and tools needed to develop your own automatic MER system based on the automatic recognition model.
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