Designing of a Novel Neural Network Model for Classification of Music Genre

Abstract

Music genre classification is an important task that entails classifying music genres based on aural data. Music genre classification is widely used in the field of music information retrieval. Data preparation, feature extraction, and classification are the three primary processes in the proposed system. New neural network is used to classify music genres. To categorize songs into respective music genres, the proposed system leverages feature values from spectrograms created from slices of songs as input to a proposed system architecture. Extensive tests on the GTZAN dataset demonstrate the efficacy of the proposed approach in comparison to existing methods. The proposed system architecture is also tested on Indian rhythms. This paper consists of the comparison of proposed system architecture with existing algorithms.