I'm having a bit of difficulty here.
I'm attempting to build a Finite Impulse Response filter to use in a Virtual 3D environment (for the sake of argument we should just call it a game), using a measured set of Impulse responses.
I'm trying to build a pair of Head-Related-Transfer-Function filters that can be used with headphones to help a player localize the 3d position of audio in the real world around him/her. It's an illusion the brain perceives, and you can actually pinpoint an audio source "out-of-head" in the real space around you, rather than feel like its inside of your head. (consumer products would just call it 3D sound)
I have a set of measurements, Impulse responses, taken of a KEMAR dummy mannequin that was done in an anechoic chamber at MIT. There's a library of pairs, one for each ear, of impulse responses for over 700 positions. They're grouped by elevation, and there is a measurement for each multiple of 5 degrees on the azimuth. The elevations are set every 10 degrees starting at 0 degrees.
Principle Component Analysis uses a bunch of Stats methods that I'm not really all that familiar with, but I have a mathematician helping me with my research. But I understand a bit of Regression analysis and that will be a big part.
The impulse responses are in raw data audio format. Which is great for mapping and creating a convolution filter, but i dont 'want to convolve the audio with the impulse reponses, I want to derive a mathematical function from the audio data. If we look at the measurements as a sample of a population, where the population would be any point between measurements, as well as the measurements themselves, then they act kind of like a standard deviation model. My big question for those of you who have done Principle Component Analysis before.... How the hell do I get raw Audio data set as some sort of Data Set for statistical analysis? I'm so stuck on this and I have no idea how to proceed.