Music Genre Classification: Training an AI model

TL;DR

This paper evaluates various machine learning models, including neural networks and ensemble methods, for music genre classification using audio signal features like MFCCs and Fourier transforms.

Contribution

It compares the performance of multiple ML algorithms for genre classification, including models built from scratch and standard implementations.

Findings

Neural networks and ensemble models show high accuracy in genre classification.

Feature extraction methods like MFCCs are effective for audio signal processing.

Model robustness varies across different algorithms.

Abstract

Music genre classification is an area that utilizes machine learning models and techniques for the processing of audio signals, in which applications range from content recommendation systems to music recommendation systems. In this research I explore various machine learning algorithms for the purpose of music genre classification, using features extracted from audio signals.The systems are namely, a Multilayer Perceptron (built from scratch), a k-Nearest Neighbours (also built from scratch), a Convolutional Neural Network and lastly a Random Forest wide model. In order to process the audio signals, feature extraction methods such as Short-Time Fourier Transform, and the extraction of Mel Cepstral Coefficients (MFCCs), is performed. Through this extensive research, I aim to asses the robustness of machine learning models for genre classification, and to compare their results.