Improvised Sequence Generation in North Indian Classical Music
Is the capability to improvise only available to humans? Is it possible to generate musical phrases using natural language processing and machine learning? Is it possible to improvise in North Indian classical music using constructs like N-gram models and neural networks? In this paper we propose two methods to generate improvised sequences for North Indian classical music. In first method, we propose a bi-gram language model to generate improvised sequences. We discuss the limitations of this approach. In second method, we propose a LSTM-RNN based model to generate improvised sequences. We discuss the motivation for this method. We discuss model architecture and training settings. We also discuss the limitations of this approach. To assess these approaches, we introduce Pt. Ajoy Chakraborty Bhoopali Rāga Compositions Dataset. We evaluate the bigram model on this dataset to achieve a TOP-1 accuracy of 46.71 percent and a TOP-3 accuracy of 83.81 percent. We also evaluate LSTM-RNN based model on Pt. Ajoy Chakraborty Bhoopali Compositions Dataset. We achieve TOP-1 accuracy of 51.43 percent and TOP-3 accuracy of 81.21 percent.
Keywords - North Indian classical music, Improvised Sequence Generation, Bigram model, LSTM-RNN