Conventional post-filtering (CPF) algorithms often use a fixed filter bandwidth to estimate the auto-spectra and the cross-spectrum. This paper first studies the drawback of the CPF algorithms under the stochastic model and discusses the ways to improve the performances of the CPF algorithms. To improve noise reduction without introducing audible speech distortion, we propose a novel spectral estimator, which is based on the structure of the noise power spectral density (NPSD). The proposed spectral estimator is applied to improve the performance of the CPF. Experimental results verify that the proposed algorithm is better than the CPF algorithms in terms of the segmental signal-to-noise-ratio improvement and the noise reduction, especially the noise reduction, is about 6 dB higher than the CPF.
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