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

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing, Entertainment, Music
Cover of the book From Content-based Music Emotion Recognition to Emotion Maps of Musical Pieces by Jacek Grekow, Springer International Publishing
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Author: Jacek Grekow ISBN: 9783319706092
Publisher: Springer International Publishing Publication: November 2, 2017
Imprint: Springer Language: English
Author: Jacek Grekow
ISBN: 9783319706092
Publisher: Springer International Publishing
Publication: November 2, 2017
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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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