Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising

Chinese Journal of ElectronicsVolume 29, Issue 5 p. 926-936 ArticleFree Access Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising Tingting Wang, Tingting Wang Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this authorHaiyan Guo, Haiyan Guo Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this authorBin Lyu, Bin Lyu Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this authorZhen Yang, Corresponding Author Zhen Yang yangz@njupt.edu.cn Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this author Tingting Wang, Tingting Wang Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this authorHaiyan Guo, Haiyan Guo Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this authorBin Lyu, Bin Lyu Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this authorZhen Yang, Corresponding Author Zhen Yang yangz@njupt.edu.cn Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing, 210003 ChinaSearch for more papers by this author First published: 01 September 2020 https://doi.org/10.1049/cje.2020.08.008AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The paper investigates the hidden relationships among speech samples by applying graph tools. Specifically, we first estimate an applicable graph topology for unstructured speech signals, which can map speech signals into the vertex domain successfully and construct as Speech graph signals (SGSs). On the basis, we define a new graph Fourier transform for SGSs, which can investigate its related graph Fourier analysis. Moreover, we propose a new Graph structure spectral subtraction (GSSS) method for speech enhancement under different noisy environments. Simulation results show that the performance of the GSSS method can be significantly improved than the classical Basic spectral subtraction (BSS) method in terms of the average Segmental signal-to-noise ratio (SSNR), Perceptual evaluation of speech quality (PESQ) and the computational complexity. Volume29, Issue5September 2020Pages 926-936 RelatedInformation