A comparative study of the difference between MFCC and PLP in the recognition of sound

Abstract

Sound is one of the most important tools for classification, recognition and identification of objects in the environment. The raw sound signal is complex and is not suitable to be feed as input to the sound identification system; hence the need for a good front-end arises. The identification rate using the RNN classifier and MFCC is 72.7%, 73.7%, 78.9% 57.1% and 58.3% for aircraft, car, rain, thunder and train respectively as compared to what was obtained by using MLP. 31.6%, 19.4%, 18.5%, 38.0% and 26.4% decline is achieved for aircraft, car, rain, thunder and train respectively when comparing between MLP and RNN for MFCC. As far as sound recognition using the input used in this experiment is concerned, MFCC outperforms PLP and MFCC & PLP using MLP as classifier.