Tabla to Drumset
Translating Rhythmic Language through Machine Learning
Tabla is known for its complex and expressive rhythms—but what if we could make those patterns playable on a drum kit in real time? This talk introduces a system that listens to tabla performances, classifies each stroke using machine learning, and responds with a drum groove that follows the flow of the original rhythm.
Built with real-time stroke detection, pattern prediction, and cross-instrument mapping, the system acts as a rhythm translator. Instead of copying tabla phrases, it interprets them—helping non-tabla players groove with its unique phrasing and energy.
Whether you're into Indian classical music, drum machines, or creative AI workflows, this talk will show how rhythm can move across traditions and tools—and how machine learning can help bridge that gap.
Shreya Gupta
Shreya Gupta is a musician, currently pursuing a Master’s in Sound Design at the University of Edinburgh. With a background in Indian classical music, composition, and audio programming, her curiosity has led her to experiment at the intersection of tradition, technology, and cross-cultural collaboration. With a deep interest in the rhythmic systems of tabla, she has developed a machine learning–based system that interprets and translates tabla patterns. This project asks whether the algorithmic logic embedded in tabla improvisation can be made accessible to musicians from outside the tradition.