Books > Computing & IT > Applications of computing > Audio processing
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Music Emotion Recognition (Hardcover)
Loot Price: R3,041
Discovery Miles 30 410
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Music Emotion Recognition (Hardcover)
Series: Multimedia Computing, Communication and Intelligence
Expected to ship within 12 - 19 working days
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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 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.
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