CRC Press, 2011. — 262 p.This book provides a comprehensive introduction to the research on modeling humans’ emotion perception of music, a research topic that emerges in the face of the explosive growth of digital music. Automatic recognition of the perceived emotion of music allows users to retrieve and organize their music collections in a fashion that is more content-centric than conventional metadata-based methods. Building such a music emotion recognition system, however, is challenging because of the subjective nature of emotion perception. One needs to deal with issues such as the reliability of ground truth data and the difficulty in evaluating the prediction result, which do not exist in other pattern recognition problems such as face recognition and speech recognition. This book provides the details of the methods that have been developed to address the issues related to the ambiguity and granularity of emotion description, the heavy cognitive load of emotion annotation, the subjectivity of emotion perception, and the semantic gap between low-level audio signal and high-level emotion perception. To the best of our knowledge, this is the first book dedicated to automatic music emotion recognition. It is aimed at students and researchers in the fields of computer science, engineering, psychology, and musicology and industrial practitioners in mobile multimedia, database management, digital home, computer–human interaction, and music information retrieval. The reader will learn from this book basic multidisciplinary knowledge of music and emotion and gain inspiration about next-generation multimedia retrieval systems. In addition, this book provides the technical details of implementing the techniques introduced in this book, twelve example MATLAB_ codes, and more than 360 useful references. Therefore, this book can be used as a guidebook for computer scientists and engineers in the development of automatic music emotion recognition systems.Introduction Overview of Emotion Description and Recognition Music Features Dimensional MER by Regression Ranking-Based Emotion Annotation and Model Training Fuzzy Classification of Music Emotion Personalized MER and Groupwise MER Two-Layer Personalization Probability Music Emotion Distribution Prediction Lyrics Analysis and Its Application to MER Chord Recognition and Its Application to MER Genre Classification and Its Application to MER Music Retrieval in the Emotion Plane Future Research Directions
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