This paper describes a MIR-based system for live musical performance between a human and a robot. This project involves combining human computer interface with musical robotics using query/retrieval architecture to create musical rhythmic phrases towards improvisation.

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 a system enabling a human to perform music with a robot in real-time, in the context of North Indian classical music. We modify a traditional acoustic sitar into a hyperinstrument in order to capture performance gestures for musical analysis. A custom built four-armed robotic Indian drummer was built using a microchip, solenoids, aluminum and folk frame drums. Algorithms written towards “intelligent” machine musicianship are described. The final goal of this research is to have a robotic drummer accompany a professional human sitar player live in performance.

Most automatic music transcription research is concerned with producing sheet music from the audio signal alone. However, the audio data does not include certain performance data which is vital for the preservation of instrument performance techniques and the creation of annotated guidelines for students. We propose the use of modified traditional instruments enhanced with sensors which can obtain such data; as a case study we examine the sitar. 1

Abstract—This paper describes experimentation using a variety of sensor techniques to capture body gestures and train a student performing the North Indian hand drums known as the Tabla. A comparative study of motion capture systems, wearable accelerometer units, and wireless inertial sensor packages, is described. Each acquisition method has it advantages and disadvantages which are explored through trial and error. The paper describes a number of applications using real-time signal processing techniques for analysis, performance, performer posture detection and machine perception of human interaction. I.