Download Audio-Based Gesture Extraction on the ESITAR Controller
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.
Download A Framework for Sonification of Vicon Motion Capture Data
This paper describes experiments on sonifying data obtained using the VICON motion capture system. The main goal is to build the necessary infrastructure in order to be able to map motion parameters of the human body to sound. For sonification the following three software frameworks were used: Marsyas, traditionally used for music information retrieval with audio analysis and synthesis, CHUCK, an on-the-fly real-time synthesis language, and Synthesis Toolkit (STK), a toolkit for sound synthesis that includes many physical models of instruments and sounds. An interesting possibility is the use of motion capture data to control parameters of digital audio effects. In order to experiment with the system, different types of motion data were collected. These include traditional performance on musical instruments, acting out emotions as well as data from individuals having impairments in sensor motor coordination. Rhythmic motion (i.e. walking) although complex, can be highly periodic and maps quite naturally to sound. We hope that this work will eventually assist patients in identifying and correcting problems related to motor coordination through sound.