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Multi-channel CNN-Based Rāga Recognition in Carnatic Music Using Sequential Aggregation Strategy

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Abstract

A vital aspect of Indian classical music (ICM) is rāga, which serves as a melodic framework for compositions and improvisations for both traditions of classical music. In this work, we propose a CNN-based sliding window analysis on mel-spectrogram and modgdgram for rāga recognition in Carnatic music. The important contribution of the work is that the proposed method neither requires pitch extraction nor metadata for the estimation of rāga. CNN learns the representation of rāga from the patterns in the mel-spectrogram/modgdgram during training through a sliding-window analysis. We train and test the network on the sliced-mel-spectrogram/modgdgram of the original audio while the final inference is performed on the audio as a whole. The performance is evaluated on 15 rāgas from the CompMusic dataset. Two fusion paradigms, namely multi-channel and multi-modal frameworks, have been implemented to identify the potential of two feature representations. Out of the two approaches, multi-modal architecture reports a macro-F1 measure of 0.72, which is at par with the performance of the baseline sequence classification model. The performance is also compared with that of a transfer learning approach.

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Data Availability Statement

The dataset employed in the proposed work is available in the CompMusic repository,

Notes

  1. A rasika, in Carnatic music terminology, is a person who has some knowledge of Carnatic music and can appreciate Carnatic music.

  2. Twelve notes are not sufficient for an accurate or faithful representation of Carnatic music; the seasoned musician can easily identify many more than 12 musical entities in an octave, and they are collectively called “melodic atoms”.

  3. swara is a Sanskrit word that connotes a breath simultaneously, a vowel and the sound of a musical note corresponding to its name.

  4. Like a scale has numbers placed at different positions and we can measure the distance between them, the swara scale also has fixed positions of the 7 swaras as mentioned above.

  5. Arohana is the ascending scale of notes, and Avarohana is the descending scale of notes.

  6. Convolution of signals in the time domain is reflected as a summation in the group delay domain.

  7. The (anti) resonance peaks (due to complex conjugate (zero) pole pairs) of a signal are better resolved in the group delay domain than in the spectral domain.

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  1. Rajeev Rajan and Sreejth Sivan have contributed equally to this work.

Authors and Affiliations

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

    Rajeev Rajan

  2. Department of Electrical and Computer Engineering, University of Washington, Seattle, USA

    Sreejth Sivan

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  1. Rajeev Rajan
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Modgdgram is the visual representation of modified group delay functions with time and frequency in the horizontal and vertical axis, respectively.

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Rajan, R., Sivan, S. Multi-channel CNN-Based Rāga Recognition in Carnatic Music Using Sequential Aggregation Strategy. Circuits Syst Signal Process 42, 4072–4095 (2023). https://doi.org/10.1007/s00034-023-02301-w

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