Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/136687
Title: Instance-based genre-specific music emotion prediction with an EEG setup
Authors: Liu, Xiaoyu
Phyo Wai, Aung Aung
Kumaran, Shastikk
Saravanan, Yukesh Ragavendar
Lin, Zhiping
Keywords: Engineering::Electrical and electronic engineering
Social sciences::Psychology
Issue Date: 2018
Source: Liu, X., Phyo Wai, A. A., Kumaran, S., Saravanan, Y. R., & Lin, Z. (2018). Instance-based genre-specific music emotion prediction with an EEG setup. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2092-2095. doi:10.1109/EMBC.2018.8512630
Abstract: This paper explores a novel direction in music-induced emotion (music emotion) analysis - the effects of different genres on the prediction of music emotion. We aim to compare the performance of various classifiers in the prediction of the emotion induced by music, as well as to investigate the adaptation of advanced features (such as asymmetries) in improving classification accuracy. The study is supported by real-world experiments where 10 subjects listened to 20 musical pieces from 5 genres- classical, heavy metal, electronic dance music, pop and rap, during which electroencephalogram (EEG) data were collected. A maximum 10-fold cross-validation accuracy of 98.4% for subject-independent and 99.0% for subject-dependent data were obtained for the classification of short instances of each song. The emotion of popular music was shown to have been most accurately predicted, with a classification accuracy of 99.6%. Further examination was conducted to investigate the effect of music emotion on the relaxation of subjects while listening.
URI: https://hdl.handle.net/10356/136687
ISBN: 9781538636466
DOI: 10.1109/EMBC.2018.8512630
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/EMBC.2018.8512630
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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