Exploring deep learning based methods for information retrieval in Indian classical music

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Industrial Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Madhusudhan, Sathwik Tejaswi

Contributors dc:contributor

• Chowdhary, Girish
• Sreenivas, Ramavarapu

• Deep Learning
• Machine Learning
• Music Information Retrieval

Statement dc:rights

• Copyright 2019 Sathwik Tejaswi Madhusudhan

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104915

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104915