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

WANG Tingting; GUO Haiyan; LYU Bin; YANG Zhen

doi:10.1049/cje.2020.08.008

Volume 29 Issue 5

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2020 > 29(5): 926-936

WANG Tingting, GUO Haiyan, LYU Bin, et al., “Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising,” Chinese Journal of Electronics, vol. 29, no. 5, pp. 926-936, 2020, doi: 10.1049/cje.2020.08.008

Citation:

WANG Tingting, GUO Haiyan, LYU Bin, et al., “Speech Signal Processing on Graphs: Graph Topology, Graph Frequency Analysis and Denoising,” Chinese Journal of Electronics, vol. 29, no. 5, pp. 926-936, 2020, doi: 10.1049/cje.2020.08.008

Citation:

PDF( 760 KB)

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

doi: 10.1049/cje.2020.08.008

Broadband Wireless Communication and Sensor Network Technology Key Lab, Nanjing University of Posts and Telecommunications, Nanjing 210003, China

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.

More Information

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

Abstract

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-tonoise ratio (SSNR), Perceptual evaluation of speech quality (PESQ) and the computational complexity.
- Graph signal processing,
- Speech,
- Graph Fourier transform,
- Graph topology,
- Denoising

FullText(HTML)

References(40)

References

Shuman D I., Narang S K. and Frossard P., "The emerging field of signal processing on graphs:Extending highdimensional data analysis to networks and other irregular domains", IEEE Signal Processing Magazine, Vol.30, No.2, pp.83-98, 2013.

Chamon LFO. and Ribeiro A., "Greedy sampling of graph signals", IEEE Transactions on Signal Processing, Vol.66, No.1, pp.34-47, 2017.

Sandryhaila A. and Moura J M F., "Big data analysis with signal processing on graphs:Representation and processing of massive data sets with irregular structure", IEEE Signal Processing Magazine, Vol.31, No.5, pp.80-90, 2014.

Ortega A., Frossard P. and Kovacevic J., "Graph signal processing:overview, challenges, and applications", Proceedings of the IEEE, Vol.106, No.5, pp.808-828, 2018.

Deri J. and Moura J M F., "Spectral projector-based graph fourier transforms", IEEE Journal of Selected Topics in Signal Processing, Vol.11, No.6, pp.785-795, 2017.

Girault B., Ortega A. and Narayanan SS., "Irregularityaware graph fourier transform", IEEE Transactions on Signal Processing, Vol.66, No.21, pp.5746-5761, 2018.

Marques A., Segarra S. and Leus G., ‘Sampling of graph signals with successive local aggregations", IEEE Transactions on Signal Processing, Vol.64, No.7, pp.1832-1847, 2015.

Chepuri S P. and Leus G., "Graph sampling for covariance estimation", IEEE Transactions on Signal and Information Processing over Networks, Vol.3, No.3, pp.451-466, 2017.

Lorenzo P D., et al., "Adaptive graph signal processing:Algorithms and optimal sampling strategies", IEEE Transactions on Signal Processing, Vol.66, No.13, pp.3584-3598, 2018.

Chamon LFO. and Ribeiro A., "Greedy sampling of graph signals", IEEE Transactions on Signal Processing, Vol.66, No.1, pp.34-47,2018.

Isufi E., Loukas A. and Simonetto A., "Autoregressive moving average graph filtering", IEEE Transactions on Signal Processing, Vol.65, No.2, pp.274-288, 2017.

David B. H., Tay. and Zhiping Lin, "Design of near orthogonal graph filter banks", IEEE Signal Processing Letters, Vol.22, No.6, pp.701-704, 2015.

Teke O. and Vaidyanathan P P., "Extending classical multirate signal processing theory to graphs-Part I:Fundamentals", IEEE Transactions on Signal Processing, Vol.65, No.2, pp.409-422, 2017.

Chen S., Varma R. and Singh A., "Signal recovery on graphs:Fundamental limits of sampling strategies", IEEE Transactions on Signal and Information Processing Over Networks, Vol.2, No.4, pp.539-554, 2015.

Chen S., Sandryhaila A. and Moura J M F., "Signal recovery on graphs:Variation minimization", IEEE Transactions on Signal Processing, Vol.53, No.17, pp.4609-4624,2015.

Sandryhaila A., Moura. and José M. F., "Discrete signal processing on graphs", IEEE Transactions on Signal Processing, Vol.61, No.7, pp.1644-1656, 2013.

Sandryhaila A., Moura. and José M. F., "Discrete signal processing on graphs frequency analysis", IEEE Transactions on Signal Processing, Vol.62, No.12, pp.3042-3054, 2014.

Sandryhaila A., Moura. and José M. F., "Discrete signal processing on graphs graph Fourier transform", 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2013.

Chen S., Varma R. and Sandryhaila A., "Discrete signal processing on graphs:Sampling theory", IEEE Transactions on Signal Processing, Vol.63, No.24, pp.6510-6523, 2015.

Marques A., Segarra S. and Leus G., "Sampling of graph signals with successive local aggregations", IEEE Transactions on Signal Processing, Vol.64, No.7, pp.1832-1843, 2015.

Segarra S., Marques A G. and Leus G., "Sampling of graph signals:Successive local aggregations at a single node", 201549th Asilomar Conference on Signals, Systems and Computers, IEEE, 2015.

Segarra S., Marques A G. and Leus G., "Aggregations sampling of graph signals in the presence of noise", 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, IEEE, 2015.

Tsitsvero M., Barbarossa S. and Di Lorenzo P., "Signals on graphs:Uncertainty principle and sampling", IEEE Transactions on Signal Processing, Vol.64, No.18, pp.4845-4860, 2016.

Grassi F., Loukas A. and Nathanaël Perraudin., "A timevertex signal processing Framework:scalable processing and meanungful representation for time-series on graph", IEEE Transactions on Signal, Vol.66, No.3, pp.817-829, 2018.

Loukas A., Foucard D., "Frequency analysis of temporal graph signals", 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2016.

Qiu K., Mao X. and Shen X., "Time-varying graph signal reconstruction", IEEE Journal of Selected Topics in Signal Processing, Vol.11, No.6, pp.870-883, 2017.

Sardellitti S., Barbarossa S. and Di Lorenzo P., "On the graph Fourier transform for directed graphs", IEEE Journal of Selected Topics in Signal Processing, Vol.11, No.6, pp.796-911, 2017.

West D B, "Introduction to graph theory", Introduction to Graph Theory, 2004.

Keith L E., "Graph theory with applications (revised edition), by 1977. SBN 0333226941(Macmillan)", Mathematical Gazette.. 1977.

Sardellitti S, Barbarossa S and Di Lorenzo P, "On the graph Fourier transform for directed graphs", IEEE Journal of Selected Topics in Signal Processing, Vol.11, No.6, pp.796-911, 2017.

Sardellitti S., Barbarossa S. and Di Lorenzo P., "Graph Fourier transform for drection graphs based on Lovasz extension of min-cut", 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2017.

Segarra S. and Yuhao W., "Joint inference of networks from stationary graph signal", 2017 Asilomar Conference on Signals, Systems and Computers, IEEE, 2017.

Gavili A. and Zhang X P., "On the shift operator, graph frequency and optimal filtering in graph signal processing", IEEE Transactions on Signal Processing, Vol.65, No.23, pp.6303-6318, 2017.

Udrea R M., Ciochina S. and Vizireanu D N., "Reduction of background noise from affected speech using a spectral subtraction algorithm based on masking properties of the human ear", International Conference on Telecommunications in Modern Satellite, IEEE, 2006.

AS. Boll, "Suppression of acoustic noise in speech using spectral subtraction", IEEE Transactions on acoustics", IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.27, No.2, pp. 113-120, 1979.

I.-T. Recommendation, "Perceptual evaluation of speech quality (pesq):An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs", Rec. ITU-T P.862(2001).

Garofolo J S., Lamel L F. and Fisher W M., "DARPA TIMIT acoustic-phonetic continous speech corpus CD-ROM", NIST speech disc 1-1.1, NASA STI/Recon Technical Report, 1993.

Longting Xu, Zhen Yang and Linhui Sun, "Simplification of Ivector extraction for speaker identification", Chinese Journal of Electronics, Vol.25, No.6, pp.1121-1126, 2016.

Yunyun Ji, Zhen Yang and Qian Xu, "Compressed speech signal sensing based on the structured block sparsity with partial knowledge of support", Chinese Journal of Electronics, Vol.29, No.1-2, pp.62-71, 2016.

Haiyan Guo, Zhen Yang, Weiping Zhu, et al., "Single-channel speech separation by l0 optimization using Quasi-KLT bases", Chinese Journal of Electronics, Vol.21, No.3, pp.535-540, 2012.

Relative Articles

Supplements(0)

Cited By

Proportional views