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Automated tabla syllable transcription using image processing techniques

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Abstract

In this paper, we have proposed an automated tabla syllable transcription method using image processing technique. As for a beginner tabla learner, the learning is faster by visualizing things rather than just listening. Therefore, we have adopted this technique for our study. We have used a human perception based approach for learning tabla and implemented the same. We have created three regions of interest for each drum, dayan and bayan. The placement of the fingers’ image feature over this region is tracked to determine the exact region where it strikes and produces a particular syllable. Each frame is initially labeled to a syllable. Finally, we have used supervised classification to prune the labeling for each stroke based on its image for a particular syllable by comparing incoming frames to the reference image using the structural similarity index. Based on this the syllables are classified and automatic transcription is done. Using the proposed method, we are proficiently able to transcript 97.14% of the tabla syllables with F1 score of 0.98.

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Authors and Affiliations

  1. Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, India

    Raghavendra Bhalarao & Mitesh Raval

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  1. Raghavendra Bhalarao
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  2. Mitesh Raval
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Correspondence to Raghavendra Bhalarao.

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Bhalarao, R., Raval, M. Automated tabla syllable transcription using image processing techniques. Multimed Tools Appl 79, 28885–28899 (2020). https://doi.org/10.1007/s11042-020-09417-0

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