Non intrusive codec identification algorithm
Dushyant Sharma, Patrick A. Naylor, Nikolay D. Gaubitch, Mike Brookes · 2012
We present a non-intrusive data driven method for codec detection and identification in the presence of background noise. The method uses a number of speech features which are then used to train a CART classifier. We demonstrate the performance of the method using several different noise types over a wide range of SNRs. Our results show that we can identify a codec and its bit rate to an accuracy of 92% and we are able to detect the presence of a codec with an accuracy of 97% at -5 dB SNR.