Noise Cancellation Using a Novel Self-adaptive Neuro-fuzzy Inference System (SANFIS)

  • Laxmipriya Samal
  • Debashisa SamalEmail author
  • Badrinarayan Sahu
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 109)


This article proposes a new method of noise cancellation using self-adaptive neuro-fuzzy inference system (SANFIS). In this method, a mathematical model is suggested between noise source and the equivalent un-measurable interference signal objective of which is to remove the interfering noise component. Many researchers used linear filtering in real-world application for noise cancellation. By using SANFIS methodology, we move forward the concept of adaptive noise cancellation entering into nonlinear area. The performance of SANFIS technique compared with previously proposed LMS and NLMS algorithm.


Noise cancellation Noise source LMS NLMS SANFIS 


  1. 1.
    Sambur M (1978) Adaptive noise canceling for speech signals. IEEE Trans Acoust Speech Signal Process 26(5):419–423CrossRefGoogle Scholar
  2. 2.
    Burgess JC (1986) Adaptive signal processing. In: Widrow B, Stearns SD (eds)Google Scholar
  3. 3.
    Wu JD, Lin SL (2010) Audio quality improvement of vehicular hands-free communication using variable step-size affine-projection algorithm. Int J Wavelets Multiresolut Inf Process 8(06):875–894CrossRefGoogle Scholar
  4. 4.
    Sasaoka N, Shimada K, Sonobe S, Itoh Y, Fujii K (2009) Speech enhancement based on adaptive filter with variable step size for wideband and periodic noise. In: IEEE international midwest symposium on circuits and systems, August 2009, pp 648–652Google Scholar
  5. 5.
    Ahmad MS, Kukrer O, Hocanin A (2013) A 2-D recursive inverse adaptive algorithm. SIViP 7(2):221–226CrossRefGoogle Scholar
  6. 6.
    Kim PU, Lee Y, Cho JH, Kim MN (2011) Modified adaptive noise canceller with an electrocardiogram to enhance heart sounds in the auscultation sounds. Biomed Eng Lett 1(3):194CrossRefGoogle Scholar
  7. 7.
    Widrow B, Glover JR, McCool JM, Kaunitz J, Williams CS, Hearn RH, Goodlin RC (1975) Adaptive noise cancelling: principles and applications. Proc IEEE 63(12):1692–1716CrossRefGoogle Scholar
  8. 8.
    Haykin S (2008) Adaptive filter theory. In: 27th annual international conference of the engineering in medicine and biology society. IEEE Press, Pearson Education India, pp 1212–1215Google Scholar
  9. 9.
    Albert TR, Abusalem H, Juniper MD (1991) Experimental results: detection and tracking of low SNR sinusoids using real-time LMS and RLS lattice adaptive line enhancers. Naval Ocean Systems Center, San Diego, CAGoogle Scholar
  10. 10.
    Kazemi R, Farsi A, Ghaed MH, Karimi-Ghartemani M (2008) Detection and extraction of periodic noises in audio and biomedical signals using Kalman filter. Sig Process 88(8):2114–2121CrossRefGoogle Scholar
  11. 11.
    Akingbade KF (2014) Separation of digital audio signals using least-mean-square (LMS) adaptive algorithm. Int J Electr Comput Eng 4(4):2088–8708Google Scholar
  12. 12.
    Dhiman J, Ahmad S, Gulia K (2013) Comparison between adaptive filter algorithms (LMS, NLMS and RLS). Int J Sci Eng Technol Res (IJSETR) 2(5):1100–1103Google Scholar
  13. 13.
    Dixit S, Nagaria D (2017) LMS adaptive filters for noise cancellation: a review. Int J Electr Comput Eng 7(5):2088–8708Google Scholar
  14. 14.
    Haykin S (2008) Adaptive filter theory. In: 27th Annual international conference of the engineering in medicine and biology society. IEEE Press, Pearson Education India, pp 1212–1215Google Scholar
  15. 15.
    Paulo SD (2008) Adaptive filtering algorithms and practical implementation. Int Series Eng Comput Sci 23–50Google Scholar
  16. 16.
    Mohammed J (2012) A study on the suitability of genetic algorithm for adaptive channel equalization. Int J Electr comput Eng 2(3):285Google Scholar
  17. 17.
    Juang CF, Chiou CT, Lai CL (2007) Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition. IEEE Trans Neural Netw 18(3):833–843CrossRefGoogle Scholar
  18. 18.
    Theocharis JB (2006) A high-order recurrent neuro-fuzzy system with internal dynamics: Application to the adaptive noise cancellation. Fuzzy Sets Syst 157(4):471–500MathSciNetCrossRefGoogle Scholar
  19. 19.
    Qin H, Yang SX (2007) Adaptive neuro-fuzzy inference systems based approach to nonlinear noise cancellation for images. Fuzzy Sets Syst 158(10):1036–1063MathSciNetCrossRefGoogle Scholar
  20. 20.
    Chandrakar C, Kowar MK (2012) Denoising ECG signals using adaptive filter algorithm. Int J Soft Comput Eng (IJSCE) 2(1):120–123, 2088–8708Google Scholar
  21. 21.
    Dixit S, Nagaria D (2017) Design and analysis of cascaded LMS adaptive filters for noise cancellation. Circuits Syst Signal Process 36(2):742–766CrossRefGoogle Scholar
  22. 22.
    Prasetyowati SAD, Susanto A (2015) Multiple processes for least mean square adaptive algorithm on roadway noise cancelling. Int J Electr Comput Eng 5(2):355Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Laxmipriya Samal
    • 1
  • Debashisa Samal
    • 1
    Email author
  • Badrinarayan Sahu
    • 1
  1. 1.Department of ECEITER, S‘O’A Deemed to be UniversityBhubaneswarIndia

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