Deep Learning Based Music Genre Classification Using Spectrogram

Abstract

Music genre classification is a vital activity that involves categorizing music genres from audio data. In the field of music information retrieval, music genre classification is frequently utilized. The proposed framework deals with three main steps: data preprocessing, feature extraction, and classification. Convolutional neural network (CNN) is the method used to tackle music genre classification. The proposed system uses feature values of spectrograms generated from slices of songs as the input into a CNN to classify the songs into their music genres. A recommendation system is also implemented after the classification process. The recommendation system aims to recommend songs on each user’s preferences and interests. Extensive experiments carried out on the GTZAN dataset show the effectiveness of the proposed system with respect to other methods.