Skip to main content

Advertisement

Springer Nature Link
Log in

An optimized frequency response masking reconfigurable filter to enhance the performance of the hearing aid system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In general, a filter bank scheme is used to distort sound signals in a hearing aid system based on the characteristics of the patient's hearing loss. Furthermore, various filter banks are mainly designed to help deaf patients. However, the existing filter bank designs use more power, higher matching error, delay, and complex design. In this paper, the optimized reconfigurable filter is planned by a novel African buffalo-based Frequency Response Masking Reconfigurable Filter (AB-FRMRF) model for enhancing the efficiency of the hearing aid system. Here, the African Buffalo (AB) manner is utilized in the filter bank for tuning parameters like matching loss, delay, and power. Here, the proposed AB-FRMRF filter model is designed with the use of MATLAB and Xilinx software. Subsequently, the performance metrics investigation of the audiogram matching has enhanced the reduction of matching error as 1.2 dB and 2.5 ms delay. The developed approach reduced the computational complexity and attained better results in frequency, delay, and power, which are compared with existing conventional methods.

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
Algorithm 1
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

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Vanitha Lakshmi M, Sudha S (2020) Noise diminution and formant extraction on vowels for hearing aid users. Multimed Tools Appl 79:3729–3741. https://doi.org/10.1007/s11042-018-6914-4

    Article  Google Scholar 

  2. Ilyas M, Othmani A, Nait-ali A (2020) Computer-aided prediction of hearing loss based on auditory perception. Multimed Tools Appl 79:15765–15789. https://doi.org/10.1007/s11042-020-08910-w

    Article  Google Scholar 

  3. Huang Yb, Tf C, Qy Z et al (2022) Encrypted speech perceptual hashing authentication algorithm based on improved 2D-Henon encryption and harmonic product spectrum. Multimed Tools Appl 81:25829–25852. https://doi.org/10.1007/s11042-022-12746-x

    Article  Google Scholar 

  4. Hassan A (2019) Enhancing Signal Detection in Frequency Selective Channels by Exploiting Time Diversity in Inter-symbol Interference Signal. Wirel Pers Commun 106(3):1373–1395. https://doi.org/10.1007/s11277-019-06220-5

    Article  Google Scholar 

  5. Roy S, Chandra A (2021) A Survey of FIR Filter Design Techniques: Low-complexity, Narrow Transition-band and Variable Bandwidth. Integration 77:193–204. https://doi.org/10.1016/j.vlsi.2020.12.001

    Article  Google Scholar 

  6. Elkhouly A, Rahim HA, Abdulaziz N, Abd Malek MF (2020) Modelling Audiograms for People with Dementia Who Experience Hearing Loss Using Multiple Linear Regression Method. In 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), IEEE. https://doi.org/10.1109/CCCI49893.2020.9256679

  7. Xu W, Zhang Z, You X, Zhang C (2020) Reconfigurable and Low-Complexity Accelerator for Convolutional and Generative Networks Over Finite Fields. IEEE Trans Comput-Aided Des Integr Circuits Syst 39(12):4894–4907. https://doi.org/10.1109/TCAD.2020.2973355

    Article  Google Scholar 

  8. Joshi H, Darak S, Kumar A (2020) Low-Complexity Reconfigurable and Intelligent Ultrawideband Angular Sensing. IEEE Syst J 14(4):4931–4942. https://doi.org/10.1109/JSYST.2019.2962714

    Article  Google Scholar 

  9. Sudharman S, Bindiya T (2020) Design of Reconfigurable FRM Channelizer using Resource Shared Non-maximally Decimated Masking Filters. J Signal Process Syst 93(8):913–922. https://doi.org/10.1007/s11265-020-01615-1

    Article  Google Scholar 

  10. Zappone A, Di Renzo M, Shams F, Qian X, Debbah M (2021) Overhead-Aware Design of Reconfigurable Intelligent Surfaces in Smart Radio Environments. IEEE Trans Wirel Commun 20(1):126–141. https://doi.org/10.1109/TWC.2020.3023578

    Article  Google Scholar 

  11. Amir A, Inani R, Elias E (2016) Reconfigurable low complexity hearing aid system using adjustable filter bank. In 2016 IEEE Region 10 Conference (TENCON), IEEE. https://doi.org/10.1109/TENCON.2016.7848526

  12. Tiwari N, Pandey P (2019) Sliding-band dynamic range compression for use in hearing aids. Int J Speech Technol 22(4):911–926. https://doi.org/10.1007/s10772-019-09635-4

    Article  Google Scholar 

  13. NagaJyothi G, Sridevi S (2019) High speed low area OBC DA based decimation filter for hearing aids application. Int J Speech Technol 23(1):111–121. https://doi.org/10.1007/s10772-019-09660-3

    Article  Google Scholar 

  14. Hareesh V, Bindiya TS (2020) Analysis of Different Rational Decimated Filter Banks Derived From the Same Set of Prototype Filters. IEEE Trans Signal Process 68:1923–1936. https://doi.org/10.1109/TSP.2020.2980148

    Article  MathSciNet  Google Scholar 

  15. Ma T, Shen C, Wei Y (2019) Adjustable filter bank design for hearing aids system. In 2019 IEEE international symposium on circuits and systems (ISCAS), IEEE. https://doi.org/10.1109/ISCAS.2019.8702314

  16. Mahesh V, Shahana T (2019) Constrained least square nonuniform dynamic filter bank for delay and Hardware efficient digital hearing aids. Health Technol 9(3):355–363. https://doi.org/10.1007/s12553-018-0268-9

    Article  Google Scholar 

  17. Amir A, Bindiya TS, Elias E (2018) Design and implementation of reconfigurable filter bank structure for low complexity hearing aids using 2-level sound wave decomposition. Biomed Signal Process Control 43:96–109. https://doi.org/10.1016/j.bspc.2018.02.020

    Article  Google Scholar 

  18. Amir A, Bindiya TS, Elias E (2019) Low-complexity implementation of efficient reconfigurable structure for cost-effective hearing aids using fractional interpolation. Comput Electr Eng 74:391–412. https://doi.org/10.1016/j.compeleceng.2019.02.008

    Article  Google Scholar 

  19. Wei Y, Ma T, Ho B, Lian Y (2019) The Design of Low-Power 16-Band Nonuniform Filter Bank for Hearing Aids. IEEE Trans Biomed Circuits Syst 13(1):112–123. https://doi.org/10.1109/TBCAS.2018.2888860

    Article  Google Scholar 

  20. Fan X, Sun T, Chen W, Fan Q (2020) Deep neural network based environment sound classification and its implementation on hearing aid app. Measurement 159:107790. https://doi.org/10.1016/j.measurement.2020.107790

    Article  Google Scholar 

  21. Zhang W, Fan X, Gao L, Liu F, Chen T (2019) Design of Low-Complexity IFRM-UMFB Architecture for Wideband Digital Receivers. Circuits Syst Signal Process 39(1):344–362. https://doi.org/10.1007/s00034-019-01177-z

    Article  Google Scholar 

  22. Panhalkar A, Doye D (2021) Optimization of decision trees using modified African buffalo algorithm. J King Saud Univ - Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2021.01.011

    Article  Google Scholar 

  23. Ramya R, Moorthi S (2019) Frequency response masking based FIR filter using approximate multiplier for bio-medical applications. Sādhanā 44(11). https://doi.org/10.1007/s12046-019-1186-x

  24. Sebastian A, Ragesh MN, James TG (2014) A low complex 10-band non-uniform FIR digital filter bank using frequency response masking technique for hearing aid. In 2014 First International Conference on Computational Systems and Communications (ICCSC), IEEE. https://doi.org/10.1109/COMPSC.2014.7032641

  25. Park S, Meher P (2014) Efficient FPGA and ASIC Realizations of a DA-Based Reconfigurable FIR Digital Filter. IEEE Trans Circuits Syst II Express Briefs 61(7):511–515. https://doi.org/10.1109/TCSII.2014.2324418

    Article  Google Scholar 

  26. Sushma C, Nimmagadda P (2024) Design of Efficient Alterable Bandwidth FIR Filterbank for Hearing aid System. e-Prime-Advances in Electrical Engineering, Electronics and Energy 7:100478. https://doi.org/10.1016/j.prime.2024.100478

  27. Westhausen NL, Meyer BT (2023) Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids. In 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE.https://doi.org/10.1109/WASPAA58266.2023.10248154

  28. Yin Y, Chen F (2023) Acoustic Feedback Cancellation Algorithm for Hearing Aids Based on a Weighted Error Adaptive Filter. Electronics 12(7):1528. https://doi.org/10.3390/electronics12071528

    Article  Google Scholar 

  29. Rawandale US, Ganorkar SR, Kolte MT (2023) VHDL based Design of an Efficient Hearing Aid Filter using an Intelligent Variable-Bandwidth-Filter. Int J Adv Comput Sci Appl 14(1)

  30. Garg A (2023) Speech enhancement using long short term memory with trained speech features and adaptive wiener filter. Multimed Tools Appl 82(3):3647–3675. https://doi.org/10.1007/s11042-022-13302-3

    Article  Google Scholar 

  31. Jadda A, Prabha IS (2023) Adaptive Weiner filtering with AR-GWO based optimized fuzzy wavelet neural network for enhanced speech enhancement. Multimed Tools Appl 82(16):24101–24125. https://doi.org/10.1007/s11042-022-14180-5

    Article  Google Scholar 

  32. Goli P, van de Par S (2023) Deep learning-based speech specific source localization by using binaural and monaural microphone arrays in hearing aids. IEEE/ACM Trans Audio Speech Lang Process 31:1652–1666. https://doi.org/10.1109/TASLP.2023.3268734

    Article  Google Scholar 

Download references

Acknowledgements

This work is funded by Savitribai Phule Pune University, Pune under IQAC ASPIRE Research Mentorship Grant scheme

Author information

Authors and Affiliations

  1. Electronics & Telecommunication Department, Pimpri Chinchwad College of Engineering, Nigdi, Pune-411044, India

    Anjali A. Shrivastav & Mahesh T. Kolte

Authors
  1. Anjali A. Shrivastav
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Mahesh T. Kolte
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Anjali A. Shrivastav.

Ethics declarations

Conflict of interest

The authors declare that they have no potential conflict of interest.

Ethical approval

All applicable institutional and/or national guidelines for the care and use of animals were followed.

Informed consent

For this type of study formal consent is not required.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shrivastav, A.A., Kolte, M.T. An optimized frequency response masking reconfigurable filter to enhance the performance of the hearing aid system. Multimed Tools Appl 83, 85357–85389 (2024). https://doi.org/10.1007/s11042-024-19491-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-19491-3

Keywords