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Analytical Comparison of Classification Models for Raga Identification in Carnatic Classical Instrumental Polyphonic Audio

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Abstract

“MUSIC is the divine way of portraying the most beautiful about this world”. With that being said, the diversity in this language of music is immense, to say the least. Broadly, one would be well aware of the classification between Indian classical music and western music. In music Information Retrieval (MIR), raga classification has a tremendous role in understanding the fundamentals of Indian classical music and in a multitude of other tasks like database organisation of music files to music recommendation systems. The paper incorporates a variety of techniques like ANN, CNN, Bi LSTM and XGBoost models for the task of Raga Identification from a Carnatic Classical Instrumental audio (CCIA). The work is initially carried out on a set of 10 ragas and then extended to largely available 15 ragas of the dataset. The data samples for the same were obtained from the standard data set. This task showed state-of-the-art results with an accuracy of 97% for a set of 15 Ragas. The astounding results were obtained without performing source separation on the musical audio track. The process was carried out on the Ragas pertaining to Carnatic Classical music, a division of Indian classical music.

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Authors and Affiliations

  1. Department of ECE, PES University, Bangalore, 560085, Karnataka, India

    Ashwini Bhat & A. Vijaya Krishna

  2. Department of CSE, PES University, Bangalore, 560085, Karnataka, India

    Sathwik Acharya

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  1. Ashwini Bhat
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  2. A. Vijaya Krishna
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  3. Sathwik Acharya
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Correspondence to Ashwini Bhat.

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“This article is part of the topical collection “Computational Statistics” guest edited by Anish Gupta, Mike Hinchey, Vincenzo Puri, Zeev Zalevsky and Wan Abdul Rahim”.

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Bhat, A., Krishna, A.V. & Acharya, S. Analytical Comparison of Classification Models for Raga Identification in Carnatic Classical Instrumental Polyphonic Audio. SN COMPUT. SCI. 1, 339 (2020). https://doi.org/10.1007/s42979-020-00355-0

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