Abstract
Motivated by the musicological relevance of tabla stroke categories in tabla accompaniment playing, we present an automatic four-way stroke classification system based on convolutional neural networks, while recognising the challenge of instrument- and style-independent classification with limited available labeled training data. Tabla stroke transcription has been traditionally viewed as a monophonic timbre recognition task given the variety of musically distinct single-drum and two-drum strokes that comprise the music. In this work, we adopt a more sound-production based approach by identifying a reduced set of ‘atomic’ strokes (damped, resonant treble and resonant bass) that serve as the primary level for classification. An advantage of this is the better exploitation of tabla training data and the potential for better generalization. The new viewpoint also facilitates exploring the acoustic similarity with Western drums via the investigation of transfer learning for the tabla task. We find that the drum pretraining learns features that are useful for our tabla stroke classification task. Further fine-tuning the model with the target tabla data leads to the expected improvements in performance, which, however, surpasses that achieved with a purely tabla-trained model for only one of the stroke categories.