Classification of Indian Classical Music (Hindustani Music) Genres through MFCCs Features using RNN-LSTM Model

Abstract

Music has been considered an inseparable part of our culture and tradition. In this work, we created a dataset with six Hindustani music genres: Abhang, Bhajan, Thumri, Tappa, Ghazal, and Kajri, each of which contains 100 songs in wave(.wav) format. To classify the Hindustani music genres, we employ the mel frequency ceptral coefficients features, which contain timbral information, and the Recurrent Neural Network-Long Short Term Memory. Our best three models achieved an average accuracy of 86% when trained on various feature sets with MFCC values of 18, 26, and 39. Furthermore, we use uniform manifold approximation and projection to transform and visualise higher-dimensional feature set data into two-dimensional space. Based on the results, we can infer that Hindustani music has more intricate melodies than western music, and feeding 18 MFCC features to the deep neural network is the optimum strategy to obtain better accuracy. Increasing the hop length from 512 to 1024 reduces the input dimension size, which facilitates the RNN-LSTM model. As a result, the performance of the RNN-LSTM models has been slightly improved. Our RNN-LSTM models’ test set accuracy decreased by 5% when we took 5 segments. Additionally, we evaluated and compared our model to six genres of the GTZAN dataset and achieved 90% accuracy.