基于噪声谱结构特性的谱减法

提出了基于噪声谱结构特性的谱减法,在不增加语音失真的情况下,抑制传统谱减法的"音乐噪声"。首先,依据噪声谱结构特性在频带间自适应平滑周期图,减小谱估计方差的同时,避免噪声非连续谱的能量泄露;其次,依据噪声谱的结构特性,对增益函数进行自适应调整以更有效的抑制有调噪声。测试结果表明,不论对宽带噪声还是对窄带噪声,本文算法在信噪比提高和噪声抑制量等客观评价指标上都明显优于传统谱减法。非正式主观测听进一步验证了本文算法的有效性。

Spectral subtraction based on the structure of noise power spectral density

This paper proposes a novel spectral subtraction algorithm based on the structure of noise power spectral density, which will be herein referred as NPSD-SS, for reducing musical noise without introducing audible speech dis-tortion. First, we propose an adaptive averaging periodogram based on the structure of the noise spectrum, which will be referred NPSD-AAP, where the better performance of the NPSD-SS is achieved mainly due to that the proposed NPSD-AAP provides a low-variance and adaptive-bandwidth spectral estimator. Second, the maximum noise reduction is adaptively determined by the property of the noise spectrum to further suppress the non-continuous noise components. Objective tests show that the NPSD-SS is better than the CSS in terms of the SNR improvement and the amount of noise reduction. Informal listening tests further confirm the validity of the proposed NPSD-SS.