Automated tabla syllable transcription using image processing techniques

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,dayanandbayan. 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.