Impulse-noise suppression in speech using the stationary wavelet transform.

An approach for detecting and removing impulse noise from speech using the wavelet transform is proposed. The approach utilizes the multi-resolution property of the wavelet transform, which provides finer time resolution at higher frequencies than the short-time Fourier transform to effectively identify and remove impulse noise. The paper then describes how the impulse-detection performance is dependent on certain wavelet features and their relationships with the impulse noise and the underlying speech signal. Performance comparisons carried out with an existing method show that the wavelet approach yields much better features for detecting the impulses. To remove the impulses, an algorithm that uses the stationary wavelet transform has been developed. The algorithm uses a two-step approach where the wavelet coefficients corresponding to the impulses are suppressed in the first step and then substituted by suitable coefficients located within the vicinity of the impulse in the second step. Performance evaluations with an existing method show that the proposed algorithm gives superior results.