Speech enhancement based on iterative Wiener filter using complex speech analysis

Recently, applications of speech coding and speech recognition have been exploding; for example, cellular phones and car navigation systems in an automobile. Since these are commonly used in noisy environment, noise reduction method, viz., speech enhancement is required as a pre-processor for speech coding and recognition. Iterative Wiener filter (IWF) method has been adopted as the speech enhancement that estimates speech and noise power spectra using LPC analysis iteratively. In this paper, we propose an improved method forWiener filter algorithm by introducing the complex LPC speech analysis instead of the conventional LPC analysis. The complex speech analysis can estimate more accurate spectrum in low frequencies, thus it is expected that it can perform better for the IWF especially for babble noise or car internal noise that contains much energy in low frequencies. The objective evaluation has been performed for speech signal corrupted by white Gaussian, pink noise, babble noise or car internal noise by means of spectral distance. The results demonstrate that the proposed method can perform better for babble or car internal noise than the conventional real-valued method.