Deep Learning Approach to Joint Identification of Instrument Pitch and Raga for Indian Classical Music

Abstract

Indian classical music is broadly divided into two classifications Carnatic and Hindustani music. The concept of raga and shruthi is fundamental in both Hindustani and Carnatic styles of music. Hence, when analyzing them, the identification of shruthi and raga is of prime importance. However, identifying the pitch, raga, and instrument from an Indian classical instrumental polyphonic audio for analysis proves to be quite a tough challenge. This chapter proposes a comprehensive comparison among convolution neural networks (CNN), recurrent neural network (RNN), and XGboost. There are three distinguishing feature sets created for each task. The first feature set comprises 26 defining features extracted from the songs, while the second feature set consists of 10 of the most significant features among the 26 defining features, and the third feature set is made up of 26 features extracted from source-separated audio files. The three models were created for individual tasks, and their performance is evaluated for the three feature sets. The implemented CNN and XGboost models have had an accuracy of around 70% and 90%, respectively. The RNN model, on the other hand, showed approximately 98% percent accuracy due to its internal memory and unique features.

The three individual tasks are combined to make a single model with RNN architecture. This combined model then demonstrated an approximate accuracy of 97.2% which is close to the accuracy obtained for the individual tasks.

For the three tasks, CNN, RNN, and XGboost had favorable accuracy.