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A review on tonic estimation algorithms in indian art music

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Abstract

Tonic is one of the basic concepts in music. The tonic pitch is normally entrenched by the main performers, depending on their vocal range and the range of the accompanying instruments. Hence, it varies among artists as well as performances. Tonic identification is a key task to be performed since it plays an important role in solving other problems like raga recognition, tuning analysis, etc. In this paper, we review some of the latest tonic identification approaches in Indian art music. We study the performance of each method in the context of music tradition using the Indian art music dataset. We analyzed the performance of the cepstral-based method of tonic pitch estimation with and without optimal selection of frames, non-negative matrix factorization (NMF), frequency-ratio method, and group delay-based algorithm. Furthermore, we show that the modified group delay-based methods outperform the conventional tonic estimation methods. We also present a detailed error analysis of each method which will be helpful for future research.

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Availability of data and materials

The datasets analyzed in this manuscript are available from CompMusic project group on request.

Notes

  1. alapana is a form of manodharmam, or improvisation, that introduces and develops a raga (musical scale)

  2. http://www.shivkumar.org/music/manodharma/index.html

  3. https://en.wikipedia.org/wiki/Gamaka(music)

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Acknowledgements

We sincerely thank the CompMusic project group for sharing the dataset for the experiments.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors

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  1. Rajeev Rajan contributed equally to this work.

Authors and Affiliations

  1. College of Engineering Trivandrum, APJ Abdul Kalam Technological University, Thiruvananthapuram, India

    Aiswarya M.A. & Rajeev Rajan

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  1. Aiswarya M.A.
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  2. Rajeev Rajan
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M.A., A., Rajan, R. A review on tonic estimation algorithms in indian art music. Multimed Tools Appl 83, 38443–38463 (2024). https://doi.org/10.1007/s11042-023-17161-4

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