Skip to main content
Log in

A frequency-domain nonlinear echo processing algorithm for high quality hands-free voice communication devices

Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A frequency-domain nonlinear echo processing algorithm is proposed to improve the audio quality during double-talk periods for hands-free voice communication devices. To achieve acoustic echo cancellation (AEC), a real-time AEC algorithm based on variable step-size partitioned block frequency-domain adaptive filtering (VSS-PBFDAF) and frequency-domain nonlinear echo processing (FNLP) algorithm was employed in the DSP chip of the prototype device. To avoid divergence during double-talk periods, normalized variable step-sizes for each frequency were introduced to adjust the convergence speed. Then, the nonlinear suppression function of FNLP was applied to inhibit the residual nonlinear acoustic echo and ensure the good quality of the near-end voice. The results of the experiment with the prototype device show that the proposed algorithm achieved deeper and more stable convergence during double-talk periods compared to the NLMS, FNLMS and traditional PBFDAF algorithms. Less nonlinear acoustic echo in the output was also obtained due to the use of FNLP. A speech quality assessment based on ITU-T P.563 showed that the Sout of the proposed algorithm achieved higher scores than that of the WebRTC algorithm. In addition, the speech output of the proposed algorithm during the double-talk periods was clear and coherent.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

References

  1. Ahgren P, Jakobsson A (2006) A study of doubletalk detection performance in the presence of acoustic echo path changes. IEEE Trans Consum Electron 52(2):515–522

    Article  Google Scholar 

  2. Azpicueta-Ruiz LA, Zeller M, Figueiras-Vidal AR (2011) Adaptive combination of volterra kernels and its application to nonlinear acoustic echo cancellation. IEEE Transactions on Audio, Speech, and Language Processing 19(1):97–110

    Article  Google Scholar 

  3. Bekrani M, Khong AWH, Lotfizad M (2011) A linear neural network-based approach to stereophonic Acoustic Echo cancellation. IEEE Trans Audio Speech Lang Process 19(6):1743–1753

    Article  Google Scholar 

  4. Bernardi G, Waterschoot TV, Wouters J, Moonen M (2015) An all-frequency-domain adaptive filter with PEM-based decorrelation for acoustic feedback control. in 2015 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA):1–5

  5. Birkett AN, Goubran RA (1995) Nonlinear echo cancellation using a partial adaptive time delay neural network. in Neural Networks for Signal Processing:449–458

  6. Cecchi S, Romoli L, Piazza F (2016) Multichannel double-talk detector based on fundamental frequency estimation. IEEE SIGNAL PROCESSING LETTERS 23(1):94–97

    Article  Google Scholar 

  7. Comminiello D, Scarpiniti M, Azpicueta-Ruiz LA, Arenas-García J, Uncini A (2013) Functional link adaptive filters for nonlinear acoustic echo cancellation. IEEE Transactions on Audio Speech & Language Processing 21(7):1502–1512

    Article  Google Scholar 

  8. Comminiello D, Scarpiniti M, Azpicueta-Ruiz LA, Arenas-Garcia J, Uncini A, Full proportionate functional link adaptive filters for nonlinear acoustic echo cancellation, in European Signal Processing Conference 2017. 1145–1149.

  9. Eneman K, Moonen M (2003) Iterated partitioned block frequency-domain adaptive filtering for acoustic echo cancellation. IEEE Transactions on Speech & Audio Processing 11(2):143–158

    Article  Google Scholar 

  10. Enhanced ITU-T G.168 echo cancellation. 2000, ITU. 128.

  11. Faller C, Tournery C, Robust Acoustic ECHO Control using a simple ECHO path model, in IEEE international conference on acoustics, Speech & Signal Processing. 2006.

    Google Scholar 

  12. Fukui M, Shimauchi S, Hioka Y, Nakagawa A, Haneda Y (2014) Double-talk Robust Acoustic Echo cancellation for CD-quality hands-free videoconferencing system. IEEE Trans Consum Electron 60(3):468–475

    Article  Google Scholar 

  13. Gansler T, Gay SL, Sondhi M, Benesty J (2000) Double-talk robust fast converging algorithms for network echo cancellation. Speech & Audio Processing IEEE Transactions on 8(6):656–663

    Article  Google Scholar 

  14. Guerin A, Faucon G, Bouquin-Jeannes RL (2003) Nonlinear acoustic echo cancellation based on Volterra filters. IEEE Transactions on Speech and Audio Processing 11(6):672–683

    Article  Google Scholar 

  15. Halimeh MM, Huemmer C, Kellermann W (2019) A neural network-based nonlinear Acoustic Echo canceller. IEEE Signal Processing Letters 26(12):1827–1831

    Article  Google Scholar 

  16. Huang F, Zhang J, Zhang S (2018) Affine projection Versoria algorithm for Robust adaptive Echo cancellation in hands-free voice communications. IEEE Trans Veh Technol 67(12):11924–11935

    Article  Google Scholar 

  17. Inc. G. WebRTC. https://webrtc.org/start/#2011

  18. Jiang T, Liang R, Wang Q, Zou C, Li C (2019) An improved practical state-space FDAF with fast recovery of abrupt Echo-path changes. IEEE Access 7(1):61353–61362

    Article  Google Scholar 

  19. Jose M. Gil-Cacho M S, Toon Vanwaterschoot, Marc Moonen. Nonlinear acoustic echo cancellation based on a sliding-window leaky kernel affine projection algorithm. IEEE Trans Audio Speech Lang Process, 2013, 21(9): 1867–1878

  20. Lee GW, Lee JH, Moon JM, Kim HK (2019) Non-linear acoustic echo cancellation based on mel-frequency domain volterra filtering. 2019 IEEE International Conference on Consumer Electronics (ICCE):1–2

  21. Lei Q, Chen H, Hou J, Chen L, Dai L (2019) Deep neural network based regression approach for acoustic echo cancellation, in 4th International Conference on Multimedia Systems and Signal Processing, ICMSSP 2019, May 10, 2019 - May 12, 2019. Association for Computing Machinery: Guangzhou, China. 94-98.

  22. Li X, Jenkins WK (1996) The comparison of the constrained and unconstrained frequency-domain block-LMS adaptive algorithms. IEEE Trans Signal Process 44(7):1813–1816

    Article  Google Scholar 

  23. Liu J (2004) Efficient and robust cancellation of echoes with long echo path delay. Communications IEEE Transactions on 52(8):1288–1291

    Article  Google Scholar 

  24. Long G, Ling F, Proakis JG (1989) The LMS Algorithm with delayed coefficient adaptation. IEEE Trans Acoust Speech Signal Process 40(9):1397–1405

    Article  Google Scholar 

  25. Panda B, Kar A, Chandra M (2014) Non-linear adaptive echo supression algorithms: A technical survey. in International Conference on Communications and Signal Processing:076–080

  26. Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-Wesley

  27. Papp II, Šarić ZM, Teslic N (2011) Hands-free voice communication with TV. IEEE Trans Consum Electron 57(2):606–614

    Article  Google Scholar 

  28. Park Y-J, Park H-M (2010) DTD-free nonlinear acoustic echo cancellation based on independent component analysis. Electron Lett 46(12):866–869

    Article  Google Scholar 

  29. Schwarz A, Hofmann C, Kellermann W, (2014) Spectral feature-based nonlinear residual echo suppression, in 2013 IEEE Workshop on Applications of Signal Processing To Audio and Acoustics. New Paltz, NY. 1-4.

  30. Shynk JJ (2002) Frequency-domain and multirate adaptive filtering. IEEE Signal Process Mag 9(1):14–37

    Article  Google Scholar 

  31. Tashev IJ (2012) Coherence based double talk detector with soft decision. in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP):165–168

  32. Union T I T (2004) ITU P.563 Single-ended method for objective speech quality assessment in narrow-band telephony applications.

  33. Waterschoot TV, Moonen M (2011) Fifty years of acoustic feedback control: state of the art and future challenges. Proc IEEE 99(2):288–327

    Article  Google Scholar 

  34. Widrow B (2005) Thinking about thinking: the discovery of the LMS algorithm. IEEE Signal Process Mag 22(1):100–106

    Article  Google Scholar 

  35. Yu D, Li J (2017) Recent progresses in deep learning based acoustic models. IEEE/CAA Journal of Automatica Sinica 4(3):396–409

    Article  Google Scholar 

  36. Zhang H, Wang D, (2018) Deep learning for acoustic echo cancellation in noisy and double-talk scenarios, in 19th Annual Conference of the International Speech Communication, INTERSPEECH 2018, September 2, 2018 - September 6, 2018. International Speech Communication Association: Hyderabad, India. 3239-3243.

  37. Zhang S, Zheng WX (2017) Recursive adaptive sparse exponential functional link neural network for nonlinear AEC in impulsive noise environment. IEEE Transactions on Neural Networks & Learning Systems PP(99):1–10

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Grant 2020YFC2004003 and Grant 2020YFC2004002. The authors would like to thank the reviewers for their valuable comments that helped in significant improvement of the quality of the paper. They would also like to thank Professor Zou Cairong for the suggestions of experimental analysis and discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ruiyu Liang.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Q., Chen, X., Liang, R. et al. A frequency-domain nonlinear echo processing algorithm for high quality hands-free voice communication devices. Multimed Tools Appl 80, 10777–10796 (2021). https://doi.org/10.1007/s11042-020-10230-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-10230-y

Keywords

Navigation