In this paper, the important problem of single-channel noise reduction is treated from a new perspective. The problem is posed as a filtering problem based on joint diagonalization of the covariance matrices of the desired and noise signals. More specifically, the eigenvectors from the joint diagonalization corresponding to the least significant eigenvalues are used to form a filter, which effe...

In this paper, we address the problem of ill-conditioning Wiener filter, optimal linear minimum mean square error estimator. Computing filter involves inverse observation covariance matrix. practice, matrix has a large condition number, resulting in unreliable numerical computation filter. To issue, develop four approximate formulas using truncation technique based on principal components compo...

Th is project gives the study of the principles of Adaptive Noise Cancellation (ANC) and its Applications. Adaptive noise Cancellat ion is an alternative technique of estimat ing signals corrupted by additive noise or interference. In signal processing methods of removing noise, levels of noise rejection are not attainable without prior knowledge about speech signal and noise. But in this metho...

This paper investigates the nonlinear effects of the LeastMean Square (LMS) adaptive predictor. Traditional analysis of the adaptive filter ignores the statistical dependence among successive tap-input vectors and bounds the performance of the adaptive filter by that of the finite-length Wiener filter. It is shown that the nonlinear effects make it possible for an adaptive transversal predictio...