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

This paper describes the design of an Electronic Sitar controller, a digitally modified version of Saraswati’s (the Hindu Goddess of Music) 19-stringed, pumpkin shelled, traditional North Indian instrument. The ESitar uses sensor technology to extract gestural information from a performer, deducing music information such as pitch, pluck timing, thumb pressure, and 3-axis of head tilt to trigger real-time sounds and graphics. It allows for a variety of traditional sitar technique as well as new performance methods. Graphical feedback allows for artistical display and pedagogical feedback. The ESitar uses a programmable Atmel microprocessor which outputs control messages via a standard MIDI jack.

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.

This paper describes the design of an Electronic Sitar controller, a digitally modified version of Saraswati's (the Hindu Goddess of Music) 19-stringed, pumpkin shelled, traditional North Indian instrument. The ESitar uses sensor technology to extract gestural information from a performer, deducing music information such as pitch, pluck timing, thumb pressure, and 3-axes of head tilt to trigger real-time sounds and graphics. It allows for a variety of traditional sitar technique as well as new performance methods. Graphical feedback allows for artistic display and pedagogical feedback. The ESitar uses a programmable Atmel microprocessor which outputs control messages via a standard MIDI jack.