Exploring deep learning based methods for information retrieval in Indian classical music
Madhusudhan, Sathwik Tejaswi
Loading…
Permalink
https://hdl.handle.net/2142/104915
Description
Title
Exploring deep learning based methods for information retrieval in Indian classical music
Author(s)
Madhusudhan, Sathwik Tejaswi
Issue Date
2019-04-25
Director of Research (if dissertation) or Advisor (if thesis)
Chowdhary, Girish
Sreenivas, Ramavarapu
Department of Study
Industrial&Enterprise Sys Eng
Discipline
Industrial Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2019-08-23T20:01:16Z
Keyword(s)
Deep Learning
Machine Learning
Music Information Retrieval
Abstract
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.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
