Music Genre Classification ChatBot

Abstract

Classification of music on the basis of genre is a sub-domain of the multidisciplinary field of music information retrieval (MIR) that is gaining traction among researchers and data scientists. Even though this problem has been extensively researched and tested, the problem still lies in the foundations, as the true definition of genre still lies to the mercy of human subjectivity. In this paper, we have proposed a classification model which employs a convolutional neural network (CNN) to differentiate between audio files by assessing the visual representations of their timbral features [1]. The music genre classification model is outlined by a ChatBot model built using NLTK, which can simulate an intelligent conversation with a user, and it employs a feature that enables it to recognize and process the audio file based on the input from the user. The GTZAN dataset [2] was used for training the music genre classification model, and the so trained model for this purpose yielded an accuracy of nearly 68.9%. The accuracy so obtained is relatively better than several other classification models that we had researched. Through extensive research and constant trials, we can state, with some certainty, that such a system can be extensively used alongside several music streaming services, as it would facilitate the process of automation of the classification of songs.