Design of optimal wavelets for detecting impulse noise in speech

Abstract

Removal of impulse noise from speech in the wavelet domain has been found to be very effective due to the multi-resolution property of the wavelet transform and the ease of removing the impulses in that domain. A critical factor that affects the performance of the impulse-removal system is the effectiveness of the impulse detection algorithm. To this end, we propose a new method for designing orthogonal wavelets that are optimized for detecting impulse noise in speech. In the method, the characteristics of the impulse noise and the underlying speech signal are taken into account and a convex optimization problem is formulated for deriving the optimal wavelet for a given support size. Performance comparison with other wellknown wavelets show that the wavelets designed using the proposed method have much better impulse detection properties.Index Terms-impulsive noise detection, wavelet design, speech enhancement

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

“…The selected dictionary and the processed signals are similar, you can use a dictionary in certain combinations of atoms to better signal, the sparse decomposition method and the orthogonal decomposition de-noising effect of the method are significantly better soft/hard threshold de-noising methods, but the dictionary chooses not to the sparse decomposition and the de-noising effect of the orthogonal decomposition method will drop accordingly. Therefore, only by setting the appropriate dictionary is theoretically can achieve ideal de-noising effect [25][26][27][28][29].…”