Skip to main content

Advertisement

Springer Nature Link
Log in

Variable-Stage Cascaded Adaptive Filter Technique for Signal De-Noising Application

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Detection and recovery of noise corrupted signals using adaptive filters are becoming popular due to their application in several fields, including communication and biomedical. Adaptive filters broadly utilize the LMS algorithm since it has low computational complexity and is robust in behavior. However, it fails to achieve a faster speed of convergence and minimum steady-state MSE simultaneously. Therefore, the objective is to estimate the noise-free signal faster with improved accuracy and obtain a lower steady-state MSE with a higher convergence speed. This paper introduces a multi-stage adaptive filtering model wherein the noisy signal is analyzed through a cascade of stages. The proposed signal de-noising scheme utilizes the automatic selection of stages to be cascaded such that the steady-state MSE is minimum. Further, to enhance convergence speed, the LMS adaptive filter’s step-size is automatically adjusted at each stage. Hence, by controlling the number of stages to be cascaded automatically and utilizing a different step-size for each stage, we obtain a high convergence speed and minimum steady-state MSE. The proposed filter structure is tested for signal de-noising application to assess its performance concerning MSE, Signal-to-Noise Ratio (SNR), Average Noise Reduction (ANR), and convergence speed. The results attained have shown that the proposed filter structure provides remarkable performance improvement at different input noise levels. Further, the proposed Improved Variable-Stage (IVS) cascaded adaptive filter model employs LMS adaptive algorithm, hence offering a cost-effective and straightforward hardware implementation of ANC.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30
Fig. 31
Fig. 32
Fig. 33

Similar content being viewed by others

References

  1. N. Ahmed, D. Hush, G. Elliott, R. Fogler, Detection of multiple sinusoids using an adaptive cascaded structure. in IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 199-202, IEEE (1984)

  2. A. Awad, Impulse noise reduction in speech signal through Multi-Stage technique. Eng. Sci. Technol. Int. J. 22, 629–636 (2019)

    Google Scholar 

  3. H.J.W. Belt, H.J. Butterweck, Cascaded all-pass sections for LMS adaptive filtering. in IEEE 8th European Signal Processing Conference, pp. 1-4, IEEE (1996)

  4. D. Bismor, Extension of LMS stability condition over a wide set of signals. Int. J. Adapt. Control Signal Process. 29, 653–670 (2015)

    Article  MathSciNet  Google Scholar 

  5. Y. Chien, Design of GPS anti-jamming systems using adaptive notch filters. IEEE Syst. J. 9(2), 451–460 (2015)

    Article  Google Scholar 

  6. Y.R. Chien, S.I. Chu, A fast converging partial update LMS algorithm with random combining strategy. Circuits Syst. Signal Process. 33, 1883–1898 (2014)

    Article  MathSciNet  Google Scholar 

  7. R. Dallinger, M. Rupp, On robustness of coupled adaptive filters. in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3085–3088, IEEE (2009)

  8. S. Dixit, D. Nagaria, Design and analysis of cascaded LMS adaptive filters for noise cancellation. Circuits Syst. Signal Process. 36(2), 1–25 (2017)

    Article  Google Scholar 

  9. L.O. Eduardo, J.T. Orlando, S. Rui, New insights in adaptive cascaded FIR structure: Application to fully adaptive interpolated FIR structures. in 15th European Signal Processing Conference (2007)

  10. B. Farhang-Boroujeny, An IIR adaptive line enhancer with controlled bandwidth. IEEE Trans. Signal Process. 477–481(1997)

  11. J. Freudenberger, S. Stenzel, Suppression of engine noise harmonics using cascaded LMS filters. in ITG Symposium on Speech Communication, pp. 1–4, (2012)

  12. A. Garcés Correa, E. Laciar, H.D. Patiño, M.E. Valentinuzzi, Artifact removal from EEG signals using adaptive filters in cascade, in 16th Argentine Bioengineering Congress and the 5th Conference of Clinical Engineering. J. Phys., IOP Publishing (2007)

  13. S. Hannah Pauline, Samiappan Dhanalakshmi, R. Kumar, A. Ankita Anand, Kar Asutosh, Variable tap-length non-parametric variable step-size NLMS adaptive filtering algorithm for Acoustic Echo cancellation. Appl. Acoust., pp. 1–9, (2020)

  14. S. Haykin, B. Widrow, ( first ed.): Least-Mean-Square Adaptive Filters. Wiley, Newyork (2003)

  15. H. Huang, P. Franti, S. Rahardja, Cascaded RLS-LMS prediction in MPEG-4 lossless speech coding. IEEE Trans. Speech Speech Lang. Process. 16(3), 554–562 (2008)

    Article  Google Scholar 

  16. M. Kalamani, S. Valarmathy, M. Krishnamoorthi, Adaptive noise reduction algorithm for speech enhancement. World Acad. Sci. Eng. Technol. Int. J. Electron. Comput. Energ. Electron. Commun. Eng. 8(6), 1007–1014 (2014)

    Google Scholar 

  17. H. Kim, S. Kim, N. VanHelleputte, T. Berset, D. Geng, I. Romero, R.F. Yazicioglu, Motion artifact removal using cascade adaptive filtering for ambulatory ECG monitoring system. in IEEE Biomedical Circuits and Systems Conference, pp. 160–163 (2012)

  18. W.J. Kozacky, T. Ogunfunmi, A cascaded IIR-FIR adaptive ANC system with output power constraints. Signal Process. 456–464(2014)

  19. S. Li, S. Wu, Y. Wang, W. Guo, Y. Zhou, An improved NLMS algorithm based on speech enhancement. in IEEE Advanced Information Technology,Electronic and Automation Control Conference, pp. 896–899, (2015)

  20. A.K. Maurya, Cascade-cascade least mean square (LMS) adaptive noise cancellation. Circuits Syst. Signal Process. 37(9), 3785–3826 (2018)

    Article  Google Scholar 

  21. A. Mehmood, I.B. Muhammad, H. Ehtasham-ul, A. Laeeq, Artifacts removal from ECG signal using a multistage MNLMS adaptive algorithm. Int. J. Signal Process. Image Process. Pattern Recognit. 10(11), 13–22 (2017)

    Google Scholar 

  22. A.D. Poularikas, (First ed.), Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB\({\textregistered }\). CRC Press,Taylor and Francis Group, (2014)

  23. P. Prandoni, M. Vetterli, An FIR cascade structure for adaptive linear prediction. IEEE Trans. Signal Process. 46(9), 2566–2571 (1998)

    Article  Google Scholar 

  24. N.G. Prelcic, F.P. Gonzalez, M.E.D. Jimenez, Wavelet packet-based subband adaptive equalization. Signal Process. 1641–1662(2001)

  25. M. Sambur, Adaptive noise canceling for speech signals. IEEE Trans. Acoust. Speech Signal Process. 26(5), 419–423 (1978)

    Article  Google Scholar 

  26. S.G. Sankaran, A.A. Beex, Acoustic echo and noise canceler improvements for hands free telephones. in IEEE Southeastcon’97, pp.148-150, Engineering the new Century, New York (1997)

  27. A.H. Sayed, (First ed.): Fundamentals of adaptive Filtering, Wiley Interscience, (2003)

  28. X. Sun, S.M. Kuo, Active narrowband noise control systems using cascading adaptive filters. IEEE Trans. Speech Speech Lang. Process. 15(2), 586–592 (2007)

    Article  Google Scholar 

  29. R. Yu, C.C. Ko, Lossless compression of digital speech using cascaded RLS-LMS prediction. IEEE Trans. Speech Speech Process. 11(6), 532–537 (2003)

    Article  Google Scholar 

Download references

Author information

Author notes

  1. S. H. Pauline and R. Kumar: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Kanchipuram, Chennai, T.N, 603203, India

    S. Hannah Pauline, Samiappan Dhanalakshmi & R. Kumar

Authors
  1. S. Hannah Pauline
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samiappan Dhanalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samiappan Dhanalakshmi.

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

Pauline, S.H., Dhanalakshmi, S. & Kumar, R. Variable-Stage Cascaded Adaptive Filter Technique for Signal De-Noising Application. Circuits Syst Signal Process 41, 1972–2006 (2022). https://doi.org/10.1007/s00034-021-01868-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01868-6

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy