Predicting Perceived Music Emotions with Respect to Instrument Combinations
Viet Dung Nguyen, Quan H. Nguyen, Richard G. Freedman
Abstract
Music Emotion Recognition has attracted a lot of academic research work in recent years because it has a wide range of applications, including song recommendation and music visualization. As music is a way for humans to express emotion, there is a need for a machine to automatically infer the perceived emotion of pieces of music. In this paper, we compare the accuracy difference between music emotion recognition models given music pieces as a whole versus music pieces separated by instruments. To compare the models' emotion predictions, which are distributions over valence and arousal values, we provide a metric that compares two distribution curves. Using this metric, we provide empirical evidence that training Random Forest and Convolution Recurrent Neural Network with mixed instrumental music data conveys a better understanding of emotion than training the same models with music that are separated into each instrumental source.
BibTeX
@article{Nguyen_Nguyen_Freedman_2024, title={Predicting Perceived Music Emotions with Respect to Instrument Combinations}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26910}, DOI={10.1609/aaai.v37i13.26910}, abstractNote={Music Emotion Recognition has attracted a lot of academic research work in recent years because it has a wide range of applications, including song recommendation and music visualization. As music is a way for humans to express emotion, there is a need for a machine to automatically infer the perceived emotion of pieces of music. In this paper, we compare the accuracy difference between music emotion recognition models given music pieces as a whole versus music pieces separated by instruments. To compare the models’ emotion predictions, which are distributions over valence and arousal values, we provide a metric that compares two distribution curves. Using this metric, we provide empirical evidence that training Random Forest and Convolution Recurrent Neural Network with mixed instrumental music data conveys a better understanding of emotion than training the same models with music that are separated into each instrumental source.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Nguyen, Viet Dung and Nguyen, Quan H. and Freedman, Richard G.}, year={2024}, month={Jul.}, pages={16078-16086} }