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).

该论文研究了图信号处理理论在语音增强中的应用。通过构建图语音信号并在图 Fourier 域中分析其谱与噪声谱的差异，提出图谱减法方法来抑制噪声干扰，进而提出迭代图谱减法以进一步提高语音增强性能。实验结果表明，所提出的方法在信噪比和语音质量评价方面均优于传统的基本谱减法和迭代基本谱减法。