一种基于语音和噪声统计信息改进相位估计的语音增强算法

摘要

为改善低信噪比环境下语音的质量,论文提出了一种改进相位估计的语音增强算法.算法首先根据语音和噪声频谱的统计模型的对称性得到用先验信噪比倒数形式表示的噪声频谱估计值,然后通过分析低信噪比条件下((0)dB)相位估计对于幅度估计的重要性,利用噪声频谱估计值估计每一个频点的相位修正值,并给出了一种优化的先验信噪比估计算法,得到一种新的语音增强算法.由仿真实验给出的客观测试和非正式听音测试表明:该算法处理后取得了较好的效果,在抑制低信噪比语音增强所产生的音乐噪声的前提下,相比未改进相位估计的算法处理后的信号,语音失真度更小,语音质量有明显提高.%This study refined speech phase estimation to boost the performance of speech enhancement algorithms in low SNR environment. Firstly, the noise spectral magnitude estimator was derived from the speech magnitude estimator for the symmetry of the statistical model, then it was used to improve the phase estimation in each frequency bin, after proving the importance of phase estimation. Finally, a modified estimator for the a priori SNR is proposed. The simulated experiments indicate that significant improvement can be achieved. For the speech enhancement in low SNR environment, the algorithm obtains an obvious improvement m speech quality, with little musical noise retained.