噪声谱驱动后滤波算法

摘要

阵列语音增强中,传统的后滤波算法大多采用固定带宽的谱估计.推导了固定带宽谱估计后滤波算法的一般表达式及其估计器的概率密度函数,首次从理论上分析了在随机信号模型下采用固定带宽谱估计存在的问题,指出后滤波的频率分辨率应由噪声谱的结构特性决定,并提出了一个新的噪声谱结构特性驱动的自谱和互谱估计的后滤波算法,该算法在增加噪声抑制量的同时能避免更多的语音失真.测试实验证明,本文算法在段信噪比提高以及噪声抑制等方面都优于传统后滤波算法,尤其是噪声抑制量方面相比传统方法提高了6 dB.%Conventional post-filtering (CPF) algorithms often use a fixed filter bandwidth to estimate the auto-spectra and the cross-spectrum. The drawback of the CPF algorithm under the stochastic model and the ways to improve the performance of the CPF algorithm are studied firstly. To improve noise reduction without introducing audible speech distortion, a novel spectral estimation method based on the structure of the noise power spectral density (NPSD) is proposed. The proposed spectral estimation method is used 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.