论文标题
基于GMM的多阶段Wiener过滤，用于增强SNR的低SNR语音
GMM based multi-stage Wiener filtering for low SNR speech enhancement
论文作者
论文摘要
本文提出了一种单渠道语音增强方法，以减少噪声并在低信噪比（SNR）水平和非平稳噪声条件下增强语音。具体而言，我们专注于使用高斯混合模型（GMM）基于具有参数Wiener滤波器的多阶段过程来建模噪声。提出的噪声模型估计了更准确的噪声功率频谱密度（PSD），并且与传统的Wiener滤波方法相比，在各种噪声条件下可以更好地概括。模拟表明，所提出的方法可以在低SNR级别的语音质量（PESQ）和清晰度（Stoi）方面取得更好的性能。
This paper proposes a single-channel speech enhancement method to reduce the noise and enhance speech at low signal-to-noise ratio (SNR) levels and non-stationary noise conditions. Specifically, we focus on modeling the noise using a Gaussian mixture model (GMM) based on a multi-stage process with a parametric Wiener filter. The proposed noise model estimates a more accurate noise power spectral density (PSD), and allows for better generalization under various noise conditions compared to traditional Wiener filtering methods. Simulations show that the proposed approach can achieve better performance in terms of speech quality (PESQ) and intelligibility (STOI) at low SNR levels.
