Frequency Controlled Noise Cancellation for Audio and Hearing Purposes

  • Ali O. Abid Noor University of Technology-Iraq
Keywords: Hearing aids, noise cancellation, adaptive filtering, filter-banks


Methods for hearing aids sought to compensate for loss in hearing by amplifying signals of interest in the audio band. In real-world, audio signals are prone to outdoor noise which can be destructive for hearing aid.  Eliminating interfering noise at high speed and low power consumption became a target for recent researches. Modern hearing compensation technologies use digital signal processing which requires minimum implementation costs to reduce power consumption, as well as avoiding delay in real time processing. In this paper, frequency controlled noise cancellation (FCNC) strategy for hearing aid and audio communication is developed with low complexity and least time delay. The contribution of the current work is made by offering a method that is capable of removing inherent distortion due filter-bank insertion and assigning adaptive filtering to a particular sub-band to remove external noise. The performance of the proposed FCNC was examined under frequency-limited noise, which corrupts particular parts of the audio spectrum. Results showed that the FCNC renders noise-immune audio signals with minimal number of computations and least delay. Mean square error (MSE) plots of the proposed FCNC method reached below -30 dB compared to -25 dB using conventional sub-band method and to -10 dB using standard full-band noise canceller. The proposed FCNC approach gave the lowest number of computations compared to other methods with a total of 346 computations per sample compared to 860 and 512 by conventional sub-band and full-band methods respectively. The time delay using FCNC is the least compared to the other methods.


Download data is not yet available.


N. S. Jensen, O. Hau, J. B. B. Nielsen, S. V. Legarth, Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference, Trends in Hearing Vol. 23: pp.1–23. 2019. DOI:

K. A. Lee, W. S. Gan, S. M. Kuo, Subband Adaptive Filtering: Theory and Implementation, John Wiley & Sons Ltd (Wiltshire), pp.99-129. 2009. DOI:

M. Djendi, A. Sayoud, A New Dual Subband Fast NLMS Adaptive Filtering Algorithm for Blind Speech Quality Enhancement and Acoustic Noise Reduction, Int. Journal of Speech Technology, Vol. 22, No.2, pp. 391-406. 2019. DOI:

S. G. Kim, C.D. Yoo, T.Q. Nguyen, Alias-Free Subband Adaptive Filtering With Critical Sampling, IEEE Transactions on Signal Processing, Vol. 56, No. 5, pp.1894-1904. 2008. DOI:

H. Choiand, H. D. Bae, Subband Affine Projection Algorithm for Acoustic Echo Cancellation System, EURASIP Journal on Advances in Signal Processing, Vol. 2007, Article ID 75621, doi:10.1155/2007/75621. 2007. DOI:

K. A. Lee, W. S. Gan, Improving Convergence of the NLMS Algorithm Using Constrained Subband Updates, IEEE Signal Processing Letters, Vol. 11, No.9, pp. 736-239. 2004. DOI:

M. R. Petraglia, P. Batalheiro, Non-Uniform Subband Adaptive Filtering With Critical Sampling, IEEE Transactions on Signal Processing, Vol. 56, No. 2, pp.565-575, 2008. DOI:

C. Schüldt, F. Lindstrom, I. Claesson, A Low- Complexity Delayless Selective Subband Adaptive Filtering Algorithm, IEEE Transactions on Signal Processing, Vol. 12, pp.5840-5850, 2008.

A. O. A. Noor, I. H. Al-Hussaini, S. A. Samad, Adaptive Cancellation of Localised Environmental Noise, Jurnal Kejuruteraan, Vol. 30, No. 2, pp.179-186, 2018. DOI:

S. Haykin, Adaptive Filter Theory. Prentice Hall (New Jersey), Ed.5, pp. 160-187, 2013.

R. M. Ramli, A. O. A. Noor, S. A. Samad, Noise Cancellation Using Selectable Adaptive Algorithm for Speech in Variable Noise Environment, International Journal Speech Technology, Vol. 20, No. 3, pp. 535-542, 2017. DOI:

P. S .R. Diniz, Adaptive IIR Filters. In: Adaptive Filtering. Springer (Boston MA), pp. 411-466, 2013 DOI:

M. Radenkovic, T. Bose, Adaptive IIR Filtering of Non Stationary Signals, Elsevier Signal Processing, Vol. 81(2001), pp. 183-195. 2001.

Y. Yu, L. Lu, Z. W. Zheng, Y. Zakharov, R. C. de Lamare, Robust DCD-Based Recursive Adaptive Algorithms. IEEE Transactions on Circuits and Systems II. DOI: 10.1109/TCSII.2019.2936407, 2019. DOI:

B. K. Das, M. Chakraborty, Improved l 0 -RLS adaptive filter. Electronics Letters, Vol. 53. No 25, pp.1650. DOI: 10.1049/el.2017.3441, 2017. DOI:

M. Narasimha, Block Adaptive Filter With Time-Domain Update Using Three Transforms, IEEE Signal Processing Letters, Vol. 14, No.1, Jan 2007, pp. 51-53, 2007. DOI:

J. Liu, S.L. Grant. Proportionate Adaptive Filtering for Block-Sparse System Identification, IEEE/ACM Transactions on Audio, Speech and Language. Volume 24 Issue 4. Pp. 623-630, 2016. DOI:

S. J. Schlecht, Frequency-Dependent Schroeder Allpass Filters, Journal of Applied Sciences (MDPI), 10, 187, pp1-11, 2019. DOI:

K. D. Rao and M.N. Swamy, Digital Signal Processing. Springer Nature Singapore Pte Ltd. Chapter 11, pp693, 2018. DOI:

G. Blanchet and M. Charbit, Digital Signal and Image Processing using MATLAB, ISTE USA, pp114-150, 2006. DOI:

L. Milić, Multirate Filtering for Digital Signal Processing: MATLAB Applications, Hershey, New York, pp40-45, 2009. DOI:

J. Agnew, J. M. Thornton, Just Noticeable and Objectionable Group Delays in Digital Hearing Aids, Journal of the American Academy of Audiology, Vol.11, No. 6, pp330-336, 2000.

K. Tsuneyama, Y. Kiyoki, A Time-Series Phrase Correlation Computing System With Acoustic Signal Processing For Music Media Creation, EMITTER International Journal of Engineering Technology, Vol. 5, No. 1, pp1-15, 2017. DOI:

How to Cite
O. Abid Noor, A. (2020). Frequency Controlled Noise Cancellation for Audio and Hearing Purposes . EMITTER International Journal of Engineering Technology, 8(1), 270-290.