Instance-based genre-specific music emotion prediction with an EEG setup
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 adap...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/136687 |
| _version_ | 1811678642162368512 |
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| author | Liu, Xiaoyu Phyo Wai, Aung Aung Kumaran, Shastikk Saravanan, Yukesh Ragavendar Lin, Zhiping |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Liu, Xiaoyu Phyo Wai, Aung Aung Kumaran, Shastikk Saravanan, Yukesh Ragavendar Lin, Zhiping |
| author_sort | Liu, Xiaoyu |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:56:30Z |
| format | Conference Paper |
| id | ntu-10356/136687 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:56:30Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1366872020-01-10T05:09:57Z Instance-based genre-specific music emotion prediction with an EEG setup Liu, Xiaoyu Phyo Wai, Aung Aung Kumaran, Shastikk Saravanan, Yukesh Ragavendar Lin, Zhiping School of Electrical and Electronic Engineering 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering::Electrical and electronic engineering Social sciences::Psychology Music Emotion Electroencephalography 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. Accepted version 2020-01-10T05:09:57Z 2020-01-10T05:09:57Z 2018 Conference Paper 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 9781538636466 https://hdl.handle.net/10356/136687 10.1109/EMBC.2018.8512630 30440815 2-s2.0-85056669065 2018 2092 2095 en © 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 application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Social sciences::Psychology Music Emotion Electroencephalography Liu, Xiaoyu Phyo Wai, Aung Aung Kumaran, Shastikk Saravanan, Yukesh Ragavendar Lin, Zhiping Instance-based genre-specific music emotion prediction with an EEG setup |
| title | Instance-based genre-specific music emotion prediction with an EEG setup |
| title_full | Instance-based genre-specific music emotion prediction with an EEG setup |
| title_fullStr | Instance-based genre-specific music emotion prediction with an EEG setup |
| title_full_unstemmed | Instance-based genre-specific music emotion prediction with an EEG setup |
| title_short | Instance-based genre-specific music emotion prediction with an EEG setup |
| title_sort | instance based genre specific music emotion prediction with an eeg setup |
| topic | Engineering::Electrical and electronic engineering Social sciences::Psychology Music Emotion Electroencephalography |
| url | https://hdl.handle.net/10356/136687 |
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