Gong, X, Yuan, R, Qian, H et al. (2 more authors) (2021) Emotion Regulation Music Recommendation Based on Feature Selection. In: New Trends in Intelligent Software Methodologies, Tools and Techniques. The 20th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SoMeT 2021), 21-23 Sep 2021, Cancun, Mexico/Virtual. IOS Press , pp. 486-495. ISBN 978-1-64368-194-8
Abstract
Chinese traditional music has been proved to be effective in emotion regulation for thousands of years. Five different groups of Chinese traditional music which have been proved can regulate different emotions (Angry, Depressed, Feverish, Desperate, Sorrowful) in the literature. 54 audios features are extracted by using the Librosa library for each music group. Five features are manually selected using histogram analysis which show significant difference between the five groups of music. Combined with KNN, SVM and Deep forest classification algorithms, the five manually selected audio features are shown to have better classification performance than traditional feature selection algorithms, like PCA and LDA. We hypothesize that these five significant audio features may be the underlying basis why so such music can effectively perform emotion regulation. Based on this classification models, prototype emotion regulation music recommendation interface (TJ-ERMR) was built that can be used for music therapy. In the future, we will use this classification model to find more music to expand the initial repertoire of our music recommendation system.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2021 The authors and IOS Press. This is an author produced version of a conference paper published in New Trends in Intelligent Software Methodologies, Tools and Techniques. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Nov 2021 11:50 |
Last Modified: | 25 Jun 2023 22:49 |
Status: | Published |
Publisher: | IOS Press |
Identification Number: | 10.3233/faia210047 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180329 |