The present paper describes a quality enhancement of band-limited speech signals. In regular telephone communication, the quality of the received speech signal is degraded by band limitation. We propose an effective but simple scheme for obtaining narrowband speech signals in which the frequency components are estimated from band limited signals. The proposed method utilizes aliasing components generated by wavelet reconstruction filters in the inverse discrete wavelet transform. The results of enhancement have been verified by applying this method to speech samples via telephone lines to obtain a noticeable improvement in speech quality.

The present paper describes a quality enhancement of speech corrupted by additive background noise in a single channel system. The proposed approach is based on the introduction of perceptual criteria using a frequency-weighting filter in a subtractive-type enhancement process. This newly developed algorithm allows for an automatic adaptation in the time and frequency of the enhancement system and finds a suitable noise estimate according to the frequency of the corrupted speech. Experimental results show that the proposed approach can efficiently remove additive noise related to various types of noise corruption.

The present paper describes quality enhancement of speech corrupted by an additive background noise in a single-channel system. The proposed approach is based on the introduction of a perceptual criterion using a frequency-weighting filter in a subtractive-type enhancement process. Although this subtractive-type method is very attractive because of its simplicity, it produces an unnatural and unpleasant residual noise. Thus, it is difficult to select fixed optimized parameters for all speech and noise conditions. A new and effective algorithm is thus developed based on the masking properties of the human ear. This newly developed algorithm allows for an automatic adaptation in the time and frequency of the enhancement system and determines a suitable noise estimate according to the frequency of the noisy input speech. Experimental results demonstrate that the proposed approach can efficiently remove additive noise related to various kinds of noise corruption.