In the Spring Quarter, I took a class called Introduction to Scientific Computing. It was sort of a survey of scientific computing techniques focused on using Octave.

Our final project was to implement a Projection Pursuit algorithm to unmix two or more mixed sound signals using the same number of mixes as there were signals. The idea is simple: Given, say, two speakers and two microphones, all at different locations, if the two speakers each play different sounds then the two microphones will detect different mixes of those two sounds. Our job was to take these two mixes and "unmix" them. For simplicity's sake, we are ignoring the time differential that would occur between the different mixes.

To do this using Projection Pursuit, we project the two mixes onto a random vector in 2 dimensional space. Then we measure the kurtosis of the projected signal. Next, we do a gradient ascent, adjusting our vector to climb uphill in the direction of the greatest kurtosis. As we approach the peak, the change in kurtosis gets smaller and smaller. When we satisfy our stopping criteria (the change in kurtosis falls below some threshold) we consider a signal to be "found". Then we extract the signal that lies on that vector and we have our first unmixed sound. Next we remove that signal from the two mixes and what remains is the other sound. This works because sounds tend to be super-gaussian signals, so the kurtosis will climb fairly quickly as you search.

I have to say, it was a really cool project and sort of "magical" when the extracted sounds started playing.

My goal is to port this project to Haskell. Projection Pursuit can be implemented, I believe, in purely functional code as fairly straightforward recursion. Now there are certain niceties to doing the original in Octave: native matrix and vector manipulation, simple graphing capabilities (so you can watch it "pursue"), and fairly easy sound playing to listen to the results. I probably won't get to coding up the graphing and sound in Haskell.

I completed some portions of the matrix manipulation functions earlier in the summer, but it's not ready to post yet, and I want to write up my progress in this process.

Next time, I'll post my Octave code, and provide some graphics of the pursuit. It's pretty cool.

I have solved the problem.

ReplyDeleteStep #1: I fired your sound guy

Step #2: I re-aligned the PA and mixed it myself.

yeah, so the use of sound signals is just one example of what can be done. Just about any multi-dimensional data can be examined with this technique. It is particularly useful when the principal components are not orthogonal, as in a mixed sound signal. Stay tuned and you'll see what I mean.

ReplyDeleteFurther to the above, after trading emails with Dr. Paul Schimpf, the instructor who gave the assignment in the first place, one of the beauties of projection pursuit is that it can be used for *any* measure of the data, not just the kurtosis.

ReplyDelete