Evaluating Key Performance Indicators for Indian Music: A Machine Learning Approach

DOI

10.2991/978-94-6463-738-0_66How to use a DOI?

Keywords

Carnatic music; Machine learning; Signal machine algorithms; Mel Frequency Cepstral Coefficients and Dynamitic time wrapper

Abstract

Hindustani music and Carnatic music are the two main schools of Indian classical music. Basically, Indian music includes microtones (shruti), notes (swaras), ornamentations (alankar), melodies (ragas) and rhythmic patterns (tala). There is a vast interest developed among individuals to learn Indian music both in India and Abroad. Most of them adopt digital learning platforms for convenience and accessibility but musicians, especially those new to performing, experience stage fright or performance anxiety, which can hinder their ability to showcase their skills. Immediate and constructive feedback is crucial for improvement. The paper discusses some of the work done in this regard. The primary focus is on Carnatic music and hence, the paper also proposes to develop an intelligent model which is useful for Carnatic music learners and teachers. Some of the algorithms used are machine learning, signal machine processing and dynamic time wrapper (DTW). Because of its focus on creating characteristics from audio data that may be utilized for recognizing the phones in speech, “Mel Frequency Cepstral Coefficients” (MFCC) the most used approach for extracting features from audio signals is used. The proposed model is intended to assess the Carnatic songs sung by Carnatic music learners and not to replace the teachers. It aims at developing a subjective tool which will assist both Carnatic music learners and the teachers.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sukumar Kotian
AU  - P. S. Ambili
AU  - Abhay Tiwari
PY  - 2025
DA  - 2025/06/22
TI  - Evaluating Key Performance Indicators for Indian Music: A Machine Learning Approach
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 849
EP  - 859
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_66
DO  - 10.2991/978-94-6463-738-0_66
ID  - Kotian2025
ER  -