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Motivated by musicological applications of the four-way categorization of tabla strokes, we consider automatic classification methods that are potentially robust to instrument differences. We present a new, diverse tabla dataset suitably annotated for the task. The acoustic correspondence between the tabla stroke categories and the common popular Western drum types motivates us to adapt models and methods from automatic drum transcription. We start by exploring the use of transfer learning on adoi:10.5281/zenodo.5624489 fatcat:c5kaz4tasvbhvmipvm466xvhgi