Feature Selection Approaches for Optimising Music Emotion Recognition Methods
This work addresses the problem of optimizing music emotion recognition for researchers and practitioners by reducing noise from irrelevant features, though it is incremental as it applies existing feature selection methods to this domain.
The paper tackles the challenge of high feature dimensionality in music emotion recognition by introducing a feature selection approach to eliminate redundant features, resulting in improved performance for both Random Forest and Support Vector Regression models.
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.