This paper discusses an evolution in North Indian instruments in the designing of technology to capture gestures from a performing artist. Modified traditional instruments use sensor technology and microcontrollers to digitize gestures, enabling a computer to analyze performance to synthesize sound and visual meaning. Specifically, systems were built to capture data from three traditional North Indian instruments: the tabla (a pair of tonal hand drums), the dholak (a barrel shaped folk drum played by two people), and the sitar (a 19-stringed, gourd-shelled instrument). This paper will discuss how these instruments are modified to capture gestural movement, how these signals are mapped to sounds and graphical feedback, and show examples of the new instruments being used in live performance. The hardware is built to try and preserve the techniques passed down from generations of tradition; however, modified performance techniques with the aid of a laptop are also introduced. 1

Using sensors to extract gestural information for control parameters of digital audio effects is common practice. There has also been research using machine learning techniques to classify specific gestures based on audio feature analysis. In this paper, we will describe our experiments in training a computer to map the appropriate audio-based features to look like sensor data, in order to potentially eliminate the need for sensors. Specifically, we will show our experiments using the ESitar, a digitally enhanced sensor based controller modeled after the traditional North Indian sitar. We utilize multivariate linear regression to map continuous audio features to continuous gestural data. 1.