Abstract: For most speech enhancement process, the accuracy of the a priori SNR and the background noise estimation has great influence. This paper derives traditional MMSE-LSA algorithm based on noncausal a priori SNR estimation. In contrast to the LSA algorithm, it introduces noncausal estimation for the a priori SNR, and utilizes smoothing parameters to change the value of noise spectral which can adaptively adjust to the noise environment. Experimental results show the new algorithm yields a higher improvement in the segmental SNR, lower residual noise and better enhancement of speech quality.

Key words: noncausal a priori SNR estimation, background noise estimation, MMSE-LSA algorithm

摘要： 对于大多数的语音增强算法，先验信噪比及背景噪音频谱估计的准确与否，对语音增强的效果影响至关重要。为此，在传统MMSE-LSA算法的基础上，提出一种基于非因果先验信噪比估计的LSA改进算法，较好地弥补了传统LSA算法在先验信噪比上估计的不足，同时采用平滑系数动态更新噪音频谱值，使估计值能更好地跟踪噪音的变化。实验结果表明，改进算法能有效减少残余噪音量，提高语音分段信噪比，改善语音质量。

关键词: 非因果先验信噪比估计, 背景噪音估计, MMSE-LSA算法

CLC Number: 

• TP311.52