用几何与一致性约束改进语音增强中的相位估计，提升噪声抑制效果。
An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement
- 结合时频谱的一致性与几何约束，解决相位符号模糊问题。
- 在VB-DMD和WSJ0-CHiME3数据集上表现优于或接近主流方法。
- 适合需要高保真语音重建的降噪场景，尤其关注背景噪声抑制。
我们提出一种新颖的迭代相位估计框架——多源Griffin-Lim算法（MSGLA），用于加性噪声环境下的语音增强（SE）。核心思想是利用复数短时傅里叶变换（STFT）谱图的启发式一致性约束，解决基于几何的相位估计中常见的符号模糊问题。此外，我们基于正弦定理和余弦定理提出一种新的几何约束变体，结合噪声相位估计构建新的相位重构算法。首先通过一系列理想条件下的预言实验验证该方法的有效性；随后在VB-DMD和WSJ0-CHiME3数据集上评估性能，结果表明所提的MSGLA变体在背景噪声抑制方面表现良好，匹配或略优于现有算法，包括直接相位估计与基于DNN的符号预测方法。
原文摘要 · Abstract (English)
We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core idea is to leverage the ad-hoc consistency constraint of complex-valued short-time Fourier transform (STFT) spectrograms to address the sign ambiguity challenge commonly encountered in geometry-based phase estimation. Furthermore, we introduce a variant of the geometric constraint framework based on the law of sines and cosines, formulating a new phase reconstruction algorithm using noise phase estimates. We first validate the proposed technique through a series of oracle experiments, demonstrating its effectiveness under ideal conditions. We then evaluate its performance on the VB-DMD and WSJ0-CHiME3 data sets, and show that the proposed MSGLA variants match well or slightly outperform existing algorithms, including direct phase estimation and DNN-based sign prediction, especially in terms of background noise suppression.
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