A correlation-maximization denoising filter used as an enhancement frontend for noise robust bird call classification

In this paper, we propose a Correlation-Maximization denoising filter which utilizes periodicity information to remove additive noise in bird calls. We also developed a statistically-based noise robust bird-call classification system which uses the denoising filter as a frontend. Enhanced bird calls which are the output of the denoising filter are used for feature extraction. Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM) are used for classification. Experiments on a large noisy corpus containing bird calls from 5 species have shown that the Correlation-Maximization filter is more effective than the Wiener filter in improving the classification error rate of bird calls which have a quasi-periodic structure. This improvement results in a 4.1% classification error rate which is better than the system without a denoising frontend and a system with a Wiener filter denoising frontend.