Feature Selection Approaches for Optimising Music Emotion Recognition Methods

TL;DR

This paper presents a feature selection method to improve music emotion recognition by reducing irrelevant data, leading to better model performance and efficiency.

Contribution

It introduces a feature selection approach that enhances MER accuracy and stability by eliminating redundant features, validated with SVR and RF models.

Findings

Performance improved with feature selection for both models

Model efficiency and stability increased

Feature selection benefits general MER tasks

Abstract

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.