Comparing MFCC and MPEG-7 Audio Features for Feature Extraaction, Maximum Likelihood HMM and Entropic Prior HMM for Sports Audio Classification

We present a comparison of 6 methods for classification of sports audio. For the feature extraction we have two choices: MPEG-7 audio features and Mel-scale Frequency Cepstrum Coefficients (MFCC). For the classificaiton we also have two choices: Maximum Likelihood Hidden Markov Models (ML-HMM) and Entropic Prior HMM(EP-HMM). EP-HMM, in turn, have two variations: with and without trimming of the model parameters. We thus have 6 possible methods, each of which corresponds to a combination. Our results show that all the combinations achieve classification accuracy of around 90% with the best and the second best being MPEG-7 features with EP-HMM and MFCC with ML-HMM.