From Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces

Description

The problems it addresses include emotion representation, annotation of music excerpts, feature extraction, and machine learning. The book chiefly focuses on content-based analysis of music files, a system that automatically analyzes the structures of a music file and annotates the file with the perceived emotions. Further, it explores emotion detection in MIDI and audio files. In the experiments presented here, the categorical and dimensional approaches were used, and the knowledge and expertise of music experts with a university music education were used for music file annotation. The automatic emotion detection systems constructed and described in the book make it possible to index and subsequently search through music databases according to emotion. In turn, the emotion maps of musical compositions provide valuable new insights into the distribution of emotions in music and can be used to compare that distribution in different compositions, or to conduct emotional comparisons of different interpretations of the same composition. Front Matter ....Pages i-xiv Introduction (Jacek Grekow)....Pages 1-3 Front Matter ....Pages 5-5 Representations of Emotions (Jacek Grekow)....Pages 7-11 Human Annotation (Jacek Grekow)....Pages 13-24 Front Matter ....Pages 25-25 MIDI Features (Jacek Grekow)....Pages 27-41 Hierarchical Emotion Detection in MIDI Files (Jacek Grekow)....Pages 43-58 Front Matter ....Pages 59-59 Audio Features (Jacek Grekow)....Pages 61-74 Detection of Four Basic Emotions (Jacek Grekow)....Pages 75-83 Emotion Tracking of Radio Station Broadcasts (Jacek Grekow)....Pages 85-93 Music Emotion Maps in the Arousal-Valence Space (Jacek Grekow)....Pages 95-106 Comparative Analysis of Musical Performances by Using Emotion Tracking on the Arousal-Valence Plane (Jacek Grekow)....Pages 107-129 Back Matter ....Pages 131-138