  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.
