Dynamic Range Compression and Its Effect on Music Genre Classification

TL;DR

This study shows that applying dynamic range compression to music test samples can improve genre classification accuracy, with optimal settings varying based on training data, suggesting compression as a useful preprocessing step.

Contribution

It is the first comprehensive analysis of how dynamic range compression affects music genre classification accuracy and identifies optimal compression parameters for improved performance.

Findings

Compression improves classification accuracy by an average of 3.1%.

Optimal compression settings vary across experiments.

Compression effectiveness depends on training data.

Abstract

This paper investigates the impact of dynamic range compression (DRC) on music genre classification accuracy. By applying various compression settings to the test set of 200 songs, we aim to determine if compression can enhance the classifier's ability to discern distinct musical genres. A support vector machine (SVM) classifier was trained on the original, uncompressed dataset. The study explored the influence of threshold, ratio, knee width, attack time, release time, and makeup gain on classification performance. Our findings indicate that applying compression to the test set can indeed improve music genre classification accuracy on average by 3.1%. The optimal compression settings varied across experiments, suggesting that the effectiveness of compression depends on the training data of the model. A table of the top compression settings over 1000 train and test splits is provided. In conclusion, this research demonstrates that dynamic range compression can serve as a valuable preprocessing technique for enhancing music genre classification. The insights gained from this study can inform the development of more accurate and robust music recommendation systems.