Abstract: As Plato once rightfully said, ‘Music gives a soul to the universe, wings to the mind, flight to the imagination and life to everything.’ Music has always been an important art form, and more so in today’s science-driven world. Music genre classification paves the way for other applications such as music recommender models. Several approaches could be used to classify music genres. In this literature, we aimed to build a machine learning model to classify the genre of an input audio file using 8 machine learning algorithms and determine which algorithm is the best suitable for genre classification. We have obtained an accuracy of 91% using the XGBoost algorithm. Keywords: Machine Learning, Music Genre Classification, Decision Trees, K Nearest Neighbours, Logistic regression, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, XGBoost
Using Machine Learning to Classify Music Genre
