Music Genre Classification

Abstract

The Music Genre Classification model automatically divides music into different genres using a small number of audio files and a range of musical attributes. This topic is highly relevant to the field of music information retrieval since it provides a way to organize and analyze large amounts of music files. For MGC, standard machine learning techniques such as SVM, KNN, Decision trees, and neural networks can be applied. These algorithms are trained to recognize different musical qualities and traits, which allows them to categorize the audio files into different genres. Numerous applications show that deep learning algorithms—such as CNN, ANN, and others—perform better than conventional machine learning algorithms. Consequently, the CNN method is adjusted to perform the categorization of music files. This classifies musical genres using deep learning methods from CNN. To evaluate the effectiveness of the MGC algorithms, accuracy is used. Moreover, the impact of different algorithms on MGC performance can be compared and studied. It can be applied to automated music recommendation systems, music production, and music education.