Speech enhancement in nonstationary noise environments using noise properties

Traditional short-time spectral attenuation (STSA) speech enhancement algorithms are ineffective in the presence of highly nonstationary noise due to difficulties in the accurate estimation of the local noise spectrum. With a view to improve the speech quality in the presence of random noise bursts, characteristic of many environmental sounds, a simple post-processing scheme is proposed that can be applied to the output of an STSA speech enhancement algorithm. The post-processing algorithm is based on using spectral properties of the noise in order to detect noisy time–frequency regions which are then attenuated using a SNR-based rule. A suitable suppression rule is developed that is applied to the detected noisy regions so as to achieve significant reduction of noise with minimal speech distortion. The post-processing method is evaluated in the context of two well-known STSA speech enhancement algorithms and experimental results demonstrating improved speech quality are presented for a data set of real noise samples.