Finger Tracking Based Tabla Syllable Transcription

In this paper, a new visual-based automated tabla syllable transcription algorithm is proposed. The syllable played on the tabla depends on the manner in which various fingers strike the tabla. In the proposed approach, in first step fingers are tracked using SIFT features in all the frames. This path of fingers for various syllables are analyzed and rules are formulated. Then these rules are used to create a visual signature for different syllables. Finally, the visual signatures of all the frames are compared to the visual signature of the base frame and the frame is labeled to that syllable. Based on this the various signatures are classified into different syllables. Using the proposed method, we are able to transcript tabla syllables with 97.14% accuracy.