基于双路奇异值分解的信号降噪方法

针对常规奇异值分解对强噪声抑制效果不佳的问题，提出了一种基于双路奇异值分解的信号降噪方法。首先采用奇异熵定阶的方法对高阶噪声进行预处理，然后从双路奇异向量的相关性出发确定低阶噪声奇异向量的位置，最后将剩余的奇异值与奇异向量重构得到优化估计的降噪信号。仿真实验表明：双路SVD相比常规SVD的降噪方法在低信噪比、白噪声的环境下信噪比增益提升4.07 dB，与纯净信号波形相关系数增量提升0.11。以一段受到座舱噪声污染的语音信号为实验对象，文中方法与双通道自适应噪声抵消的降噪方法对比，信噪比增益提升4.83 dB，运算耗时缩短1.5 s。此外，文中方法不受噪声类型的限制，对于有色噪声和单频干扰甚至混合噪声同样具有良好的适应性，有广泛的应用前景。

A Noise Cancellation Methool Based on Singular Value Decomposition

Aimed at the problem that singular value decomposition for single channel is poor in the effect on strong noise, a noise cancellation method based on dual channel singular value decomposition is proposed. Firstly, the method of singular entropy determination is used to preprocess the noise component of high order, then the position of the low order singular vectors of the noise is determined from the correlation of the dual channel singular vectors, and finally, the residual singular values and corresponding singular vectors are reconstructed to obtain the optimized estimated noise free signal. The simulation experiments show that by the proposed method compared with the conventional method under condition of the low SNR and the white noise environment, the SNR gain increases by 4.07 dB and waveform correlation coefficient increment of pure signal increases by 0.11. On the other hand, a voice signal contaminated by cockpit noise is chosen as the experimental object. Compared the proposed method with the adaptive noise cancellation method for dual channel, the results show that the SNR gain increases by 4.83 dB, and the operation takes a 1.5 s shorten. In addition, the proposed method is not restricted by the noise type, and also has a good adaptability to the colored noise, the single frequency interference and even the mixed noise, and a broad application prospect.