In this paper, we investigate the application of graph signal processing (GSP) theory in speech enhancement. We first propose a set of shift operators to construct graph speech signals, and then analyze their spectrum in the graph Fourier domain. By leveraging the differences between the spectrum of graph speech and graph noise signals, we further propose the graph spectral subtraction (GSS) method to suppress the noise interference in noisy speech. Moreover, based on GSS, we propose the iterative graph spectral subtraction (IGSS) method to further improve the speech enhancement performance. Our experimental results show that the proposed operators are suitable for graph speech signals, and the proposed methods outperform the traditional basic spectral subtraction (BSS) method and iterative basic spectral subtraction (IBSS) method in terms of both signal-to-noise ratios (SNR) and mean Perceptual Evaluation of Speech Quality (PESQ).

在本文中，我们研究了图形信号处理（GSP）理论在语音增强中的应用。我们首先提出一组移位运算符来构造图形语音信号，然后在傅立叶域中分析其频谱。通过利用图语音和图噪声信号频谱之间的差异，我们进一步提出了图谱减法（GSS）方法来抑制噪声语音中的噪声干扰。此外，基于GSS，我们提出了迭代图谱减法（IGSS）方法，以进一步提高语音增强性能。我们的实验结果表明，提出的算子适用于图形语音信号，