WANG Tingting, GUO Haiyan, LYU Bin, YANG Zhen. Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising[J]. Chinese Journal of Electronics, 2020, 29(5): 926-936. doi: 10.1049/cje.2020.08.008
Citation: WANG Tingting, GUO Haiyan, LYU Bin, YANG Zhen. Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising[J]. Chinese Journal of Electronics, 2020, 29(5): 926-936. doi: 10.1049/cje.2020.08.008

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

doi: 10.1049/cje.2020.08.008
Funds:  This work is supported by the National Natural Science Foundations of China (No.61671252, No.61271335, No.61901229), the Natural Science Research of Higher Education Institutions of Jiangsu Province (No.19KJB510008), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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  • Corresponding author: YANG Zhen (corresponding author) received the B.E. and M.E. degrees from NUPT in 1983 and 1988, respectively, and the Ph.D. degree from Shanghai Jiao Tong University in 1999, all in electrical engineering. He was initially employed as a Lecturer in 1983 by NUPT, where he was promoted to Associate Professor in 1995 and then a Full Professor in 2000. His research interests include various aspects of signal processing and communication, such as communication systems and networks, cognitive radio, spectrum sensing, speech and audio processing, compressive sensing and wireless communication. (Email:yangz@njupt.edu.cn)
  • Received Date: 2019-06-10
  • Rev Recd Date: 2020-06-28
  • Publish Date: 2020-09-10
  • 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-tonoise ratio (SSNR), Perceptual evaluation of speech quality (PESQ) and the computational complexity.
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