Rage Music Classification and Analysis using K-Nearest Neighbour, Random Forest, Support Vector Machine, Convolutional Neural Networks, and Gradient Boosting

TL;DR

This paper compares various machine learning algorithms, including CNNs and ensemble methods, for classifying rage music, a subgenre of rap, and identifies key audio features that distinguish it.

Contribution

It introduces a comprehensive comparison of multiple classifiers for rage music and highlights the most effective features for genre identification.

Findings

CNNs and Gradient Boosting perform best in classification accuracy

Key audio features for rage music are identified and analyzed

Broader sonic trends in rage music are uncovered

Abstract

We classify rage music (a subgenre of rap well-known for disagreements on whether a particular song is part of the genre) with an extensive feature set through algorithms including Random Forest, Support Vector Machine, K-nearest Neighbour, Gradient Boosting, and Convolutional Neural Networks. We compare methods of classification in the application of audio analysis with machine learning and identify optimal models. We then analyze the significant audio features present in and most effective in categorizing rage music, while also identifying key audio features as well as broader separating sonic variations and trends.