Song Classification using Machine Learning

Abstract

Abstract: The classification of music by genre is crucial in the modern world since the number of music tracks, both online and offline, is growing quickly. We must appropriately index them in order to have greater access to them. To retrieve music from a vast collection, automatic music genre classification is crucial. The majority of the current methods for categorising music genres rely on machine learning. We give a music dataset with ten distinct genres in this article. The system is trained and classified using a Deep Learning technique. Convolution neural networks are employed in this instance for training and classification. For audio analysis, feature extraction is the most important step. For sound samples, the Mel Frequency Cepstral Coefficient (MFCC) is employed as a feature vector. The suggested technique uses feature vector extraction to categorise music into different genres. Our findings indicate that our system's accuracy level is approximately 76%, which will significantly increase and facilitate the automatic classification of musical genres.