Emotion Based Music Visualization with Fractal Arts

Januka Dharmapriya, Lahiru Dayarathne, Tikiri Diasena, Shiromi Arunathilake, Nihal Kodikara, Primal Wijesekera

2023

Abstract

Emotion based music visualization is an emerging multidisciplinary research concept. Fractal arts are generated by executing mathematical instructions through computer programs. Therefore in this research, several multidisciplinary concepts, various subject areas are considered and combined to generate artistic but computationally created visualizations. The main purpose of this research is to obtain the most suitable emotional fractal art visualization for a given song segment and evaluate the entertainment value generated through the above approach. Due to the novice nature of previous findings, limited availability of emotionally annotated musical databases and fractal art music visualizing tools, obtaining accurate emotional visualization using fractal arts is a computationally challenging task. In this study, Russell’s Circumplex Emotional Model was used to obtain emotional categories. Emotions were predicted using machine learning models trained with MediaEval Database for Emotional Analysis of Music. A regression approach was used with the WEKA machine learning tool for emotion prediction. Effectiveness of the results compared with several regression models available in WEKA. According to the above comparison, the random forest regression approach provided the most reliable results compared to other models (accuracy of 81% for arousal and 61% for valence). Relevant colour for the emotion was obtained using Itten’s circular colour model and it was mapped with a fractal art generated using the JWildfire Fractal Art Generating tool. Then fractal art was animated according to the song variations. After adding enhanced features to the above approach, the evaluation was conducted considering 151 participants. Final Evaluation unveiled that Emotion Based Music Visualizations with Fractal Arts can be used to visualize songs considering emotions and most of the visualizations can exceed the entertainment value generated by currently available music visualization patterns.

Paper Citation

in Harvard Style

Dharmapriya J., Dayarathne L., Diasena T., Arunathilake S., Kodikara N. and Wijesekera P. (2023). Emotion Based Music Visualization with Fractal Arts. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 111-120. DOI: 10.5220/0011929100003497

in Bibtex Style

@conference{improve23,
author={Januka Dharmapriya and Lahiru Dayarathne and Tikiri Diasena and Shiromi Arunathilake and Nihal Kodikara and Primal Wijesekera},
title={Emotion Based Music Visualization with Fractal Arts},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2023},
pages={111-120},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011929100003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Emotion Based Music Visualization with Fractal Arts
SN - 978-989-758-642-2
AU - Dharmapriya J.
AU - Dayarathne L.
AU - Diasena T.
AU - Arunathilake S.
AU - Kodikara N.
AU - Wijesekera P.
PY - 2023
SP - 111
EP - 120
DO - 10.5220/0011929100003497
PB - SciTePress