Comparing MFCC and MPEG-7 audio features for feature extraction, 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 classification we also have two choices: maximum likelihood hidden Markov models (ML-HMM) and entropic prior HMM (EP-HMM). EP-HMM, in turn, has 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.