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From Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces [electronic resource] / by Jacek Grekow.

By: Grekow, Jacek [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 747Publisher: Cham : Springer International Publishing : Imprint: Springer, 2018Edition: 1st ed. 2018.Description: XIV, 138 p. 71 illus., 22 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783319706092.Subject(s): Computational intelligence | Music | Acoustical engineering | Emotions | Pattern recognition systems | Acoustics | Computational Intelligence | Music | Engineering Acoustics | Emotion | Automated Pattern Recognition | AcousticsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Introduction -- Representations of Emotions -- Human Annotation -- MIDI Features -- Hierarchical Emotion Detection in MIDI Files.
In: Springer Nature eBookSummary: 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.
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Introduction -- Representations of Emotions -- Human Annotation -- MIDI Features -- Hierarchical Emotion Detection in MIDI Files.

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.

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