Implementation of a Supervised Learning Model for Raga Identification in Carnatic Music
Veena Kaimal, Snehlata Barde · 2021 Asian Conference on Innovation in Technology (ASIANCON) · 2021
Music is one of the most pious cultures that exist throughout the world. Almost every country has their own rich musical culture, most of them are with strong classical backgrounds. India is one such land, rich in various types of music. This research paper is about the study of the identification of Raga. ‘Raga’ is said to be the melody of a song. We have used the Audio Signal Processing (ASP) techniques for feature extraction of two very similar ragas from Carnatic Music (CM) by analyzing the ‘RagaChhaya’ swaras commonly known as ‘RagaLakshna’ swaras of these ragas using Essentia and SMS tools. Essentia is an open-source library based on C++ with python and JavaScript wrappers for extensive audio analysis and MIR studies. The ‘SMS-tools’ is an application called ‘Spectral Modelling and Synthesis’ used to analyze sound and music applications. We proposed a supervised machine learning model trained with the features extracted from the two ragas considered, using the SMS-tools, for testing and training data that compares the given input piece of the audio signal to correctly identify based on the trained model.