Choosing an accurate number of mel frequency cepstral coefficients for audio classification purpose

Abstract

In this paper, we study several audio classification schemes applied on different number of features for multiclass classification with imbalanced datasets. As features, we proposed the liftering Mel frequency cepstral coefficients, while for classification we use probabilistic methods, instance-based learning algorithms, support vector machines, neural networks, L∞-norm based classifier, fuzzy lattice reasoning classifier, and trees. The final goal is to find the appropriate number of liftering Mel frequency cepstral coefficients to provide the desired accuracy for audio classification purpose. The best results are obtained using 16 features and & k-Nearest Neighbor as a classifier. In this case, the correct classification rate is 99.79%, the false alarm rate is 0.05%, the miss rate is 0.21%, the precision is 99.80% and the F-measure is 99.79%.