  In Hindustani classical music, the tabla plays an important role as a
rhythmic backbone and accompaniment. In applications like computer-based music
analysis, learning singing, and learning musical instruments, tabla stroke
transcription, $t\bar{a}la$ identification, and generation are crucial. This
paper proposes a comprehensive system aimed at addressing these challenges. For
tabla stroke transcription, we propose a novel approach based on model-agnostic
meta-learning (MAML) that facilitates the accurate identification of tabla
strokes using minimal data. Leveraging these transcriptions, the system
introduces two novel $t\bar{a}la$ identification methods based on the sequence
analysis of tabla strokes. \par Furthermore, the paper proposes a framework for
$t\bar{a}la$ generation to bridge traditional and modern learning methods. This
framework utilizes finite state transducers (FST) and linear time-invariant
(LTI) filters to generate $t\bar{a}las$ with real-time tempo control through
user interaction, enhancing practice sessions and musical education.
Experimental evaluations on tabla solo and concert datasets demonstrate the
system's exceptional performance on real-world data and its ability to
outperform existing methods. Additionally, the proposed $t\bar{a}la$
identification methods surpass state-of-the-art techniques. The contributions
of this paper include a combined approach to tabla stroke transcription,
innovative $t\bar{a}la$ identification techniques, and a robust framework for
$t\bar{a}la$ generation that handles the rhythmic complexities of Hindustani
music.
