DeepSRGM -- Sequence Classification and Ranking in Indian Classical
  Music with Deep Learning
Sathwik Tejaswi Madhusudhan Girish Chowdhary
A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a
melodic framework for compositions and improvisations alike. Raga Recognition
is an important music information retrieval task in ICM as it can aid numerous
downstream applications ranging from music recommendations to organizing huge
music collections. In this work, we propose a deep learning based approach to
Raga recognition. Our approach employs efficient pre possessing and learns
temporal sequences in music data using Long Short Term Memory based Recurrent
Neural Networks (LSTM-RNN). We train and test the network on smaller sequences
sampled from the original audio while the final inference is performed on the
audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during
inference on the Comp Music Carnatic dataset and its 10 Raga subset
respectively making it the state-of-the-art for the Raga recognition task. Our
approach also enables sequence ranking which aids us in retrieving melodic
patterns from a given music data base that are closely related to the presented
query sequence.