Hi, Introduction: ******************** I have created an application that knows to match a collection wavelet patterns to sampled audio data ( hence, search ), This was achieved by extracting the features ( FFT, Mel-Scale & MFCC ) for the template and the searched/sampled content, and then, checking their correlation. The problem: ******************* The correlation algorithm I use correlates sampled data with a collection of templates and returns the best matching template ( with a similarity grade ). All this works fine and percice BUT when used with thousands of pattern templates it takes ALLOT of CPU, the main ( and only real ) CPU consumer is the correlation algorithm I use, that is: "Pearson's product-moment coefficient" ( as specified on http://en.wikipedia.org/wiki/Correlation ) I wonder is there any better algorithm ( in terms of CPU consumption ) for matching sampled data with a collection of templates ? I have thought of cross-correlating all of the patterns searched with each other, this cross-correlation can be calculated offline, and, can possibly be used to optimize the realtime 'search' for the best fit pattern ( in a tree structure or so ). I really have to dig deepr in to that and I really don't know how to pruff if the algorithm will converge. Any help would be appreciated Cheers, Nadav Rubinstein
Audio pattern matching
Started by ●March 12, 2007