Classification of Hindustani Musical Ragas Using One-Dimensional Convolutional Neural Networks

Abstract

Ragas are a melodic progression of notes used in Indian classical music. They are believed to have mental and physiological enriching qualities and are used in Raga music therapy. Identification of Ragas necessitates a great deal of expertise since there are instances where two or more Ragas have very similar characteristics making them difficult to identify. An accurate classifier will be an indispensable tool for Indian classical music learners and enthusiasts alike. This paper proposes a One-Dimensional Convolutional Neural Network (1D-CNN) to classify Ragas in the Hindustani variant of the Indian classical music using raw audio waveform. We compare our model with an Artificial Neural Network (ANN) trained using audio features which were extracted using traditional signal processing techniques from the audio files. The original dataset generated and annotated by an expert consists of audio files for 12 Ragas played on the 4 instruments. An augmented dataset consisting of 12,000 samples was created from the original dataset using slight pitch variation. The ANN trained using audio features and the 1D-CNN trained using raw audio show an accuracy of 97.04% and 98.67%, respectively.