Abstract
In this thesis, we present our research result of classifying audio signals, including voices from humans and frogs. For human voices, we tried to classify speech, laughter, and singing. For frog voices, we tried to identify 21 different kinds of frogs based on their voices.After extracting features based on MFCC (mel-frequency cepstral coeffocients), we used various statistic pattern recognition methods for data reduction and for speedup in nearest neighbor search. These methods include linear discriminant analysis （LDA） to reduce data dimension, k-means to reduce data size, and branch and bound tree to reduce the computation speed of nearest neighbor search . At last, we compared two methods of classification – k-nearest neighbor rule（KNN）and Gaussian mixture model（GMM）.Our research result shows that the use of LDA and k-means not only reduce the computation time but also improve the recognition rate. We also propose the best way to combine two classification methods in order to achieve the best performance.