Comparison of Parametric representations of Birdcall in Gaussian Mixture model

Abstract

This paper focuses on the methods of automatic classifications of birds into different species based on feature extraction methods & audio recordings of their sounds. The recognition system uses Gaussian mixture model (GMM) to model 14 poultry bird species calls. Mel frequency cepstral coefficients (MFCC) parameters & wavelet parameters are used for feature vector extraction. The paper briefly explains the methods & also evaluates the performance of these methods in Gaussian Mixture Model classification .The results depicts the performance of Gaussian Mixture Model classification using wavelet was more efficient in terms of percentage of accuracy at around 80% and computation was also faster.