I am happy to announce that Ståle Skogstad defended his PhD today. Ståle was a PhD student in the project Sensing Music-related Actions (SMA) in our fourMs group, and I served as one of his supervisors.
The thesis is titled Methods and Technologies for Using Body Motion for Real-Time Musical Interaction and is available from the UiO archive. Abstract:
There are several strong indications for a profound connection between musical sound and body motion. Musical embodiment, meaning that our bodies play an important role in how we experience and understand music, has become a well accepted concept in music cognition. Today there are increasing numbers of new motion capture (MoCap) technologies that enable us to incorporate the paradigm of musical embodiment into computer music. This thesis focuses on some of the challenges involved in designing such systems. That is, how can we design digital musical instruments that utilize MoCap systems to map motion to sound?
The first challenge encountered when wanting to use body motion for musical interaction is to find appropriate MoCap systems. Given the wide availability of different systems, it has been important to investigate the strengths and weaknesses of such technologies. This thesis includes evaluations of two of the technologies available: an optical marker-based system known as OptiTrack V100:R2; and an inertial sensor-based system known as the Xsens MVN suit.
Secondly, to make good use of the raw MoCap data from the above technologies, it is often necessary to process them in different ways. This thesis presents a review and suggestions towards best practices for processing MoCap data in real time. As a result, several novel methods and filters that are applicable for processing MoCap data for real-time musical interaction are presented in this thesis. The most reasonable processing approach was found to be utilizing digital filters that are designed and evaluated in the frequency domain. To determine the frequency content of MoCap data, a frequency analysis method has been developed. An experiment that was carried out to determine the typical frequency content of free hand motion is also presented. Most remarkably, it has been necessary to design filters with low time delay, which is an important feature for real-time musical interaction. To be able to design such filters, it was necessary to develop an alternative filter design method. The resulting noise filters and differentiators are more low-delay optimal than than those produced by the established filter design methods.
Finally, the interdisciplinary challenge of making good couplings between motion and sound has been targeted through the Dance Jockey project. During this project, a system was developed that has enabled the use of a full-body inertial motion capture suit, the Xsens MVN suit, in music/dance performances. To my knowledge, this is one of the first attempts to use a full body MoCap suit for musical interaction, and the presented system has demonstrated several hands-on solutions for how such data can be used to control sonic and musical features. The system has been used in several public performances, and the conceptual motivation, development details and experience of using the system are presented.