TablaNet : a real-time online musical collaboration system for Indian percussion

Abstract

Thanks to the Internet, musicians located in different countries can now aspire to play with each other almost as if they were in the same room. However, the time delays due to the inherent latency in computer networks (up to several hundreds of milliseconds over long distances) are unsuitable for musical applications. Some musical collaboration systems address this issue by transmitting compressed audio streams (such as MP3) over low-latency and high-bandwidth networks (e.g. LANs or Internet2) to constrain time delays and optimize musician synchronization. Other systems, on the contrary, increase time delays to a musically-relevant value like one phrase, or one chord progression cycle, and then play it in a loop, thereby constraining the music being performed. In this thesis I propose TablaNet, a real-time online musical collaboration system for the tabla, a pair of North Indian hand drums. This system is based on a novel approach that combines machine listening and machine learning. Trained for a particular instrument, here the tabla, the system recognizes individual drum strokes played by the musician and sends them as symbols over the network. A computer at the receiving end identifies the musical structure from the incoming sequence of symbols by mapping them dynamically to known musical constructs. To deal with transmission delays, the receiver predicts the next events by analyzing previous patterns before receiving the original events, and synthesizes an audio output estimate with the appropriate timing. Although prediction approximations may result in a slightly different musical experience at both ends, we find that this system demonstrates a fair level of playability by tabla players of various levels, and functions well as an educational tool.