户外场景下的语音通话质量受风噪的影响巨大，并且风噪具有短时波动剧烈的特性，对其进行有效抑制一直是相关风噪抑制技术的难点。本文提出一种两步骤的风噪抑制算法，充分利用不同通道风噪信号之间的不相关性和频谱分布特性来抑制风噪。第一步利用双通道信号间的差和功率比（差和比）来判断风噪是否存在，并利用双麦的空间相关性和相似性进行风噪初步抑制；第二步利用频谱质心法（质心法）和差和比估计残余风噪，再通过维纳滤波进行二级降噪。实验结果表明，在不同风速下本文提出的算法相比基线算法具有更强的风噪抑制能力，并且风噪越强效果提升越明显。
Deep learning has become a popular approach for improving acoustic echo cancellation (AEC) in communication systems. However, existing systems mostly rely on traditional delay estimation methods, which often result in performance degradation due to inaccurate delay estimation. Furthermore, most deep learning-based methods use single-stage networks, the limited learning capability of which hinders the performance under harsh echo conditions. To address these challenges, this paper proposes a two-stage system with dual-path alignment. The two-stage strategy performs echo suppression on the magnitude spectrum in the first stage, followed by phase correction in the second one. In addition, the first stage processes two parallel features, the magnitude spectrum and its exponential compressed version, with which dual-path alignment is conducted to improve delay estimation. Experiment results demonstrate the effectiveness of the proposed system for echo suppression in challenging scenarios involving long delay, double-talk and nonlinear distortion.
这篇文章主要是介绍一些传统的噪声抑制算法，比如谱减法，维纳滤波和MMSE。其中也包含了一些我个人对于传统算法的理解，还有一些对于传统与深度之间的联系的思考。