  The high feature dimensionality is a challenge in music emotion recognition.
There is no common consensus on a relation between audio features and emotion.
The MER system uses all available features to recognize emotion; however, this
is not an optimal solution since it contains irrelevant data acting as noise.
In this paper, we introduce a feature selection approach to eliminate redundant
features for MER. We created a Selected Feature Set (SFS) based on the feature
selection algorithm (FSA) and benchmarked it by training with two models,
Support Vector Regression (SVR) and Random Forest (RF) and comparing them
against with using the Complete Feature Set (CFS). The result indicates that
the performance of MER has improved for both Random Forest (RF) and Support
Vector Regression (SVR) models by using SFS. We found using FSA can improve
performance in all scenarios, and it has potential benefits for model
efficiency and stability for MER task.
