I spend a lot of time walking around the city with my daughter these days, and have been wondering how much I move and how the movement is distributed over time. To answer these questions, and to try out a method for easy and cheap motion capture, I decided to record today’s walk to the playground. I could probably have recorded the accelerometer data in my phone, but I wanted to try an even more low-tech solution: an audio recorder....
Terminology is always challenging. I have previously written about definitions of actions and gesture several times (e.g. here, here, and here) and chapter 2 in the book Musical gestures: sound, movement, and meaning (Routledge, 2010). Movement vs motion There are, however, two words/terms that I still find very challenging to define properly and to differentiate: movement and motion. In Norwegian, we only have one word (bevegelse) for describing movement/motion, which makes everything much simpler....
In between organizing a little conference, teaching (MUS2006, MUS2860, MUS4830), and finalizing some publications, I have started a new research/artistic project with Kari Anne Bjerkestrand. I’ll write a lot more on this later, but for now I just wanted to share a plot from a motion capture recording of a single marker placed on my neck (C7). The recording is of me standing still in 10 minutes. Quite a lot of motion for someone standing still… To be continued....
In video analysis I have been working with what is often referred to as “quantity of motion” (which should not be confused with momentum, the product of mass and velocity p=mv), i.e. the sum of all active pixels in a motion image. In this sense, QoM is 0 if there is no motion, and has a positive value if there is motion in any direction. Working with various types of sensor and motion capture systems, I see the same need to know how much motion there is in the system, independent of the number of variables and dimensions in the system studied....
Last week we started setting up a “black box” in the new lab space. It is great to finally have a more permanent motion lab set up that we can use for various types of observation studies and recording sessions.
I had a quick read of Jordi Janer’s dissertation today: Singing-Driven Interfaces for Sound Synthesizers. The dissertation presents a good overview of various types of voice analysis techniques, and suggestions for various ways of using the voice as a controller for synthesis. I am particularly interested in his suggestion of a GDIF namespace for structuring parameters for voice control: /gdif/instrumental/excitation/loudness x /gdif/instrumental/modulation/pitch x /gdif/instrumental/modulation/formants x1 x2 /gdif/instrumental/modulation/breathiness x /gdif/instrumental/selection/phoneticclass x...
Some pictures from the kickoff-seminar for the Sensing Music-related Actions project last week: Project leader Rolf-Inge Godøy started with a short presentation of the new project. Then Marcelo M. Wanderley (McGill, Montreal) held an overview of various types of motion capture solutions, and the pros and cons of each of them. He stressed two main challenges he had had over the years: synchronisation of various types of mo-cap data with audio, video, music notation, etc....
I held a guest lecture at the speech, music and hearing group at KTH in Stockholm a couple of weeks ago, and got a tour of the lab afterwards. There I got a demonstration of the Optitrack optical motion capture system, which, as compared to other similar systems, is an amazingly cheap solution starting at $4999. Obviously, it has lower accuracy and precision than the larger systems, but then it also costs 1/20 of the price… However, 100 Hz speed and millimeter precision is decent for a USB-based system, and the cameras are really portable (10x5 cm or so each)....
Came across a student project from Cornell on doing motion capture using accelerometers, based on the Atmel controller. It is a nice overview of many of the challenges faced when working with accelerometers, and the implementation seems to work well. {width=“300/”}
Dutch company Softkineticoffers what they call natural interfaces, i.e. interfaces where you don’t have to put on any sensors to interact: Softkinetic operates with a single depth sensing camera, requires no marker (no gamepad, no wiimote, no special gloves or clothing, no headset - nothing), and works under all lighting conditions and scene settings (at home, in a fitness center, an amusement park, a classroom, a game cafe, an industrial simulation room - anywhere....