Publication or Conference Title:M.A. Thesis, McGill University
This thesis presents a multimodal sonification system that combines video with sound synthesis generated from motion capture data. Such a system allows for a fast and efficient exploration of musicians’ ancillary gestural data, for which sonification complements conventional videos by stressing certain details which could escape one’s attention if not displayed using an appropriate representation. The main objective of this project is to provide
a research tool designed for people that are not necessarily familiar with signal processing or computer sciences. This tool is capable of easily generating meaningful sonifications thanks to dedicated mapping strategies. On the one hand, the dimensionality reduction of data obtained from motion capture systems such as the Vicon is fundamental as it may exceed 350 signals describing gestures. For that reason, a Principal Component Analysis is used to objectively reduce the number of signals to a subset that conveys the most significant gesture information in terms of signal variance. On the other hand, movement data presents high variability depending on the subjects: additional control parameters for sound synthesis are offered to restrain the sonification to the significant gestures, easily perceivable visually in terms of speed and path distance. Then, signal conditioning techniques are proposed to adapt the control signals to sound synthesis parameter requirements or to allow for emphasizing certain gesture characteristics that one finds important. All those data treatments are performed in realtime within one unique environment, minimizing data manipulation and facilitating efficient sonification designs. Realtime process also allows for an instantaneous system reset to parameter changes and process selection so that the user can easily and interactively manipulate data, design and adjust sonification strategies.