In speech communication and particularly in mobile voice communication, noise reduction is a vital task for improvement in the quality of communication. Several speech enhancement algorithms available in the literature are mainly for the reduction of noise in stationary environments. In this paper, a two-stage noise reduction algorithm is proposed, which suits both quasi-stationary and non-stationary noise environments. The algorithm is based on two-stage noise filtering, using spectral gain and perceptually motivated weighting techniques. The proposed speech enhancement algorithm and three other algorithms are used to reduce noise in different noisy speech samples, the results of which are compared and discussed.

In mobile communication systems, conversations are increasingly distorted by environment noise. Often, there occurs a situation where the speech signal is distorted because of superposed background noise. Conventional single channel speech enhancement algorithms improve the quality of noisy speech when the noise is fairly stationary and introduce the distortions in the enhanced signal. However, they do not improve the intelligibility when the enhanced signal is presented directly to a human listener. The loss of intelligibility is mostly due to distortions introduced by the noise reduction preprocessor. Several speech enhancement algorithms have been developed over the last two decades, such as Wiener filter, power spectral subtraction (Berouti et al., 1979), modified spectral subtraction method, and minimum mean- square error short-time spectral amplitude estimator (Ephraim and Malah, 1984). Improvements are still sought because the existing algorithms ensure that the spectral characteristics of the noise change very slowly, compared to those of the speech; this may not be true in non-stationary environments. In non-stationary environments, noise characteristics may change appreciably during speech activity such that speech enhancement system performance is degraded. The proposed algorithm consists of spectral gain and perceptually motivated weighting function (Anitha Sheela et al., 2006). Figure 1 illustrates the system for the proposed scheme. The algorithm includes short-time spectral amplitude estimator, a procedure for estimating and updating the noise power spectral density and for estimating the modified spectral gain and perceptual weighting filter. Conventional speech enhancement schemes were proved to be very efficient in reducing the stationary noise. But, in non-stationary environments, there will always be some noise called musical noise (Anitha Sheela et al., 2006) that lowers the speech quality. To reduce this musical noise, in this paper, perceptual weighting function is included, and this weighting function is obtained based on noise masking threshold characteristics of the human auditory system.

Telecommunications Journal, Speech Enhancement, Spectral Gain, Masking Threshold, Perceptual Weighting Function, Speech Ccommunication, Mmusical Noise, Signal-to-Noise Ratio, SNR, Modified Bark Spectral Distortion, MBSD, Sstationary Environment, Power Spectral Subtraction.