  Large scale machine learning-based Raga identification continues to be a
nontrivial issue in the computational aspects behind Carnatic music. Each raga
consists of many unique and intrinsic melodic patterns that can be used to
easily identify them from others. These ragas can also then be used to cluster
songs within the same raga, as well as identify songs in other closely related
ragas. In this case, the input sound is analyzed using a combination of steps
including using a Discrete Fourier transformation and using Triangular
Filtering to create custom bins of possible notes, extracting features from the
presence of particular notes or lack thereof. Using a combination of Neural
Networks including 1D Convolutional Neural Networks conventionally known as
Time-Delay Neural Networks) and Long Short-Term Memory (LSTM), which are a form
of Recurrent Neural Networks, the backbone of the classification strategy to
build the model can be created. In addition, to help with variations in shruti,
a long-time attention-based mechanism will be implemented to determine the
relative changes in frequency rather than the absolute differences. This will
provide a much more meaningful data point when training audio clips in
different shrutis. To evaluate the accuracy of the classifier, a dataset of 676
recordings is used. The songs are distributed across the list of ragas. The
goal of this program is to be able to effectively and efficiently label a much
wider range of audio clips in more shrutis, ragas, and with more background
noise.
