Comparison of MFCC approaches

To compare the MFCC methods, we propose to generate data from two known theoretical distributions, then build a classifier to classify the data. Knowing the theoretical distributions of the data, we can validate the projected PDFs against the known PDFs and can even compare classification performance with the optimal Neyman-Pearson classifier. To make the problem interesting, we base one theoretical model on MFCC and the other on an circular auto-regressive process, so we can compare MFCC features with AR features. To make the problem even more interesting, we make the two models as close as possible to each other - approximating the same spectral shape.