A Novel Music Genre Classification Using Convolutional Neural Network

Abstract

Because of the rise of music songs, both online and offline, genre differentiation of music is increasingly important in today s environment. This increases the need to properly catalog and get more access. Music classification is important when searching for music in a large set. Machine learning methods are used in the bulk of modern type classification techniques known as KNN, SVM, etc. This article presents the GT-ZAN music dataset. It has ten dissimilar musical genres. Deep learning technique is encoded to coach and identify the method. For training and classification, a convolution neural network is used. The most important role in the audio analysis is feature extraction. As a function vector, the Mel Frequency Cepstral constant (MFCC) is utilized for the sound sample. By extracting the feature vector, the planned framework categorizes music into varied genres. Our study found that our project has an accuracy level of about 97% for training and 74% for testing, which will significantly boost and encourage the classification of music genres.