基于奇异谱分析和维纳滤波的语音去噪算法

针对维纳滤波算法对非平稳语音信号去噪存在的信号失真、信噪比(SNR)不高的问题,提出了一种奇异谱分析(SSA)和维纳滤波(WF)相结合的语音去噪算法SSA-WF。通过奇异谱分析将非线性、非平稳的语音信号初步去噪,提高含噪语音的信噪比以获取尽可能平稳的语音,并将其作为维纳滤波的输入,以剔除其中仍存在的高频噪声,最终获取纯净的去噪语音。在不同强度的背景噪声下进行仿真实验,结果表明SSA-WF算法在SNR和均方根误差(RMSE)等方面都要优于传统的语音去噪算法,能够有效去除背景噪声,降低有用信号的失真,适用于非线性、非平稳语音信号的去噪。

Speech denoising algorithm based on singular spectrum analysis and Wiener filtering

Concerning that the Wiener filtering algorithm leads signal distortion with low Signal-to-Noise Ratio (SNR) when dealing with the noise of non-stationary speech signal, a new speech denoising algorithm named SSA-WF was proposed combining with Singular Spectrum Analysis (SSA) and Wiener Filtering (WF). To obtain the speech signal as smooth as possible, SSA was used to denoise the nonlinear and non-stationary speech signal to improve the SNR of the noisy speech. Then the processed signal was put into WF to further eliminate the high frequency noise that still existed in the speech signal. The simulation results from different intensity of background noise show that the proposed algorithm is superior to the traditional methods in SNR and Root-Mean-Square Error (RMSE). The results also demonstrate that the new algorithm can not only remove the background noise efficiently, but also reserve the details of the original signal, it is suitable for the denoising of nonlinear and non-stationary speech signal.