Skip to main content
SpringerLink
Log in

A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Next-generation audio-visual (AV) hearing aids stand as a major enabler to realize more intelligible audio. However, high data rate, low latency, low computational complexity, and privacy are some of the major bottlenecks to the successful deployment of such advanced hearing aids. To address these challenges, we propose an integration of 5G Cloud-Radio Access Network (C-RAN), Internet of Things (IoT), and strong privacy algorithms to fully benefit from the possibilities these technologies have to offer. Existing audio-only hearing aids are known to perform poorly in noisy situations where overwhelming noise is present. Current devices make the signal more audible but remain deficient in restoring intelligibility. Thus, there is a need for hearing aids that can selectively amplify the attended talker or filter out acoustic clutter. The proposed 5G IoT-enabled AV hearing-aid framework transmits the encrypted compressed AV information and receives encrypted enhanced reconstructed speech in real time to address cybersecurity attacks such as location privacy and eavesdropping. For security implementation, a real-time lightweight AV encryption is proposed, based on a piece-wise linear chaotic map (PWLSM), Chebyshev map, and a secure hash and S-Box algorithm. For speech enhancement, the received secure AV (including lip-reading) information in the cloud is used to filter noisy audio using both deep learning and analytical acoustic modelling. To offload the computational complexity and real-time optimization issues, the framework runs deep learning and big data optimization processes in the background, on the cloud. The effectiveness and security of the proposed 5G-IoT-enabled AV hearing-aid framework are extensively evaluated using widely known security metrics. Our newly reported, deep learning-driven lip-reading approach for speech enhancement is evaluated under four different dynamic real-world scenarios (cafe, street, public transport, pedestrian area) using benchmark Grid and ChiME3 corpora. Comparative critical analysis in terms of both speech enhancement and AV encryption demonstrates the potential of the envisioned technology to deliver high-quality speech reconstruction and secure mobile AV hearing aid communication. We believe our proposed 5G IoT enabled AV hearing aid framework is an effective and feasible solution and represents a step change in the development of next-generation multimodal digital hearing aids. The ongoing and future work includes more extensive evaluation and comparison with benchmark lightweight encryption algorithms and hardware prototype implementation.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Nisar S, Tariq M, Adeel A, Gogate M, Hussain A. 2019. Cognitively inspired feature extraction and speech recognition for automated hearing loss testing. Cognitive Computation, pp 1–14.

  2. Hearing Aids Market. https://www.marketsandmarkets.com/PressReleases/hearing-aids.asp. Accessed: 2019-02-15.

  3. Ruggles A J, Ekoto I W. Ignitability and mixing of underexpanded hydrogen jets. Int J Hydrogen Energy 2012;37(22):17549– 17560.

    CAS  Google Scholar 

  4. Kortlang S, Ewert S, Meister H, Rählmann S, Kießling J, et al. 2016. Combination of controlled laboratory tests and structured field trials for a comprehensive evaluation of a model-based hearing aid. Int J Audiol.

  5. Rotili R, Principi E, Squartini S, Schuller B. A real-time speech enhancement framework in noisy and reverberated acoustic scenarios. Cogn Comput 2013;5(4):504–16.

    Google Scholar 

  6. Cadore J, Valverde-Albacete FJ, Gallardo-Antolín A, Peláez-Moreno C. Auditory-inspired morphological processing of speech spectrograms: Applications in automatic speech recognition and speech enhancement. Cogn Comput 2013;5(4):426–41.

    Google Scholar 

  7. Ben Messaoud MA, Bouzid A, Ellouze N. A new biologically inspired fuzzy expert system-based voiced/unvoiced decision algorithm for speech enhancement. Cogn Comput 2016;8(3):478–93.

    Google Scholar 

  8. Kandagatla RK, Subbaiah PV. Speech enhancement using mmse estimation of amplitude and complex speech spectral coefficients under phase-uncertainty. Speech Comm 2018;96:10–27.

    Google Scholar 

  9. Siam AI, El-khobby HA, Abd Elnaby MM, Abdelkader HS, Abd El-Samie FE. A novel speech enhancement method using Fourier series decomposition and spectral subtraction for robust speaker identification. Wirel Pers Commun. 2019;1–14.

  10. Hussain A, Barker J, Marxer R, Adeel A, Whitmer W, Watt R, Derleth P. 2017. Towards multi-modal hearing aid design and evaluation in realistic audio-visual settings: challenges and opportunities.

  11. Sumby WH, Pollack I. Visual contribution to speech intelligibility in noise. J Acoust Soc Am 1954;26(2): 212–215.

    Google Scholar 

  12. Summerfield Q. Use of visual information for phonetic perception. Phonetica 1979;36(4-5):314–331.

    CAS  PubMed  Google Scholar 

  13. McGurk H, MacDonald J. Hearing lips and seeing voices. Nature 1976;264(5588):746.

    CAS  PubMed  Google Scholar 

  14. Patterson ML, Werker JF. Two-month-old infants match phonetic information in lips and voice. Dev Sci 2003;6(2):191–196.

    Google Scholar 

  15. Milner AB. Visually derived wiener filters for speech enhancement. IEEE Trans Audio Speech Lang Process 2011; 19(6):1642–1651.

    Google Scholar 

  16. Saxena N, Roy A, Sahu BJR, Kim HS. Efficient IoT gateway over 5G wireless: a new design with prototype and implementation results. IEEE Commun Mag 2017;55(2):97–105.

    Google Scholar 

  17. Al-Turjman F, Ever E, Zahmatkesh H. 2018. Small cells in the forthcoming 5G/IoT traffic modelling and deployment overview. IEEE Communications Surveys & Tutorials.

  18. Al-Turjman F. Fog-based caching in software-defined information-centric networks. Comput Electr Eng 2018; 69:54–67.

    Google Scholar 

  19. Hasan MZ, Al-Turjman F, Al-Rizzo H. Analysis of cross-layer design of quality-of-service forward geographic wireless sensor network routing strategies in green internet of things. IEEE Access 2018;6:20371–20389.

    Google Scholar 

  20. Al-Turjman F. Cognitive caching for the future sensors in fog networking. Pervasive Mob Comput 2017;42: 317–334.

    Google Scholar 

  21. Al-Turjman F, Alturjman S. Confidential smart-sensing framework in the IoT era. J Supercomput 2018;74 (10):5187–5198.

    Google Scholar 

  22. Adeel A, Gogate M, Hussain A, Whitmer WM. Lip-reading driven deep learning approach for speech enhancement. IEEE Transactions on Emerging Topics in Computational Intelligence. 2019.

  23. Adeel A, Gogate M, Hussain A. 2018. Contextual audio-visual switching for speech enhancement in real-world environments. Information Fusion (In Press). arXiv:1808.09825.

  24. Adeel A, Larijani H, Ahmadinia A. Random neural network based novel decision making framework for optimized and autonomous power control in LTE uplink system. Phys Commun 2016;19:106–117.

    Google Scholar 

  25. Einhorn R. Hearing aid technology for the 21st century: a proposal for universal wireless connectivity and improved sound quality. IEEE pulse 2017;8(2):25–28.

    PubMed  Google Scholar 

  26. Agiwal M, Roy A, Saxena N. Next generation 5G wireless networks: a comprehensive survey. IEEE Commun Surv Tutorials 2016;18(3):1617–1655.

    Google Scholar 

  27. Andrews JG, Buzzi S, Choi W, Hanly SV, Lozano A, Soong ACK, Zhang JC. What will 5G be? IEEE J Sel Areas Commun 2014;32(6):1065–1082.

    Google Scholar 

  28. Bhushan N, Li J, Malladi D, Gilmore R, Brenner D, Damnjanovic A, Sukhavasi R, Patel C, Geirhofer S. Network densification: the dominant theme for wireless evolution into 5G. IEEE Commun Mag 2014;52(2):82–89.

    Google Scholar 

  29. Chen M, Yang J, Hao Y, Mao S, Hwang K. A 5G cognitive system for healthcare. Big Data and Cognitive Computing 2017;1(1):2.

    Google Scholar 

  30. Buchanan WJ, Li S, Asif R. Lightweight cryptography methods. J Cyber Secur Technol 2017;1(3-4): 187–201.

    Google Scholar 

  31. Shannon CE. Communication theory of secrecy systems. Bell Labs Tech J 1949;28(4):656–715.

    Google Scholar 

  32. Huang X. Image encryption algorithm using chaotic Chebyshev generator. Nonlinear Dyn 2012;67(4):2411–2417.

    Google Scholar 

  33. Wang X, Luan D, Bao X. Cryptanalysis of an image encryption algorithm using Chebyshev generator. Digital Signal Process 2014;25:244–247.

    Google Scholar 

  34. Zhou Y, Bao L, Chen CLP. A new 1d chaotic system for image encryption. Signal Process 2014;97: 172–182.

    Google Scholar 

  35. Cooke M, Barker J, Cunningham S, Shao X. An audio-visual corpus for speech perception and automatic speech recognition. J Acoust Soc Am 2006;120(5):2421–2424.

    PubMed  Google Scholar 

  36. Barker J, Marxer R, Vincent E, Watanabe S. 2015. The third ‘CHIME’ speech separation and recognition challenge: dataset, task and baselines. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp 504–511. IEEE.

  37. Viola P, Jones M. 2001. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol 1, pp I–I. IEEE.

  38. Ross DA, Lim J, Lin R-S, Yang M-H. Incremental learning for robust visual tracking. Int J Comput Vis 2008;77(1-3):125– 141.

    Google Scholar 

  39. Ahmad J, Khan MA, Hwang SO, Khan JS. A compression sensing and noise-tolerant image encryption scheme based on chaotic maps and orthogonal matrices. Neural Comput Appl 2017;28(1):953–967.

    Google Scholar 

  40. Khan FA, Ahmed J, Khan JS, Ahmad J, Khan MA. 2017. A novel substitution box for encryption based on Lorenz equations. In: International Conference on Circuits, System and Simulation (ICCSS), pp 32–36. IEEE.

  41. Khan JS, Ahmad J, Khan MA. TD-ERCS map-based confusion and diffusion of autocorrelated data. Nonlinear Dyn 2017;87(1):93–107.

    Google Scholar 

  42. Ahmad J, Hwang SO. Chaos-based diffusion for highly autocorrelated data in encryption algorithms. Nonlinear Dyn 2015;82(4):1839–1850.

    Google Scholar 

  43. Anees A, Siddiqui AM, Ahmed F. Chaotic substitution for highly autocorrelated data in encryption algorithm. Commun Nonlinear Sci Numer Simul 2014;19(9):3106–3118.

    Google Scholar 

  44. Sathiyamurthi P, Ramakrishnan S. Speech encryption using chaotic shift keying for secured speech communication. EURASIP Journal on Audio Speech, and Music Processing 2017;2017(1):20.

    Google Scholar 

Download references

Acknowledgments

The authors would like to gratefully acknowledge Mandar Gogate from the University of Stirling for his contribution in implementing LSTM-driven AV mapping, which was published in our previous work and cited here for reference.

Funding

This research was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) Grant No. EP/M026981/1 and deepCI grant No.DCI1012.

Author information

Authors and Affiliations

  1. University of Wolverhampton, Wolverhampton, WV1 1LY, UK

    Ahsan Adeel

  2. deepCI.org, 20/1 Parkside Terrace, Edinburgh, EH16 5XW, UK

    Ahsan Adeel

  3. Glasgow Caledonian University, Glasgow, G4 0BA, UK

    Jawad Ahmad & Hadi Larijani

  4. Edinburgh Napier University, Edinburgh, EH10 5DT, UK

    Amir Hussain

Authors
  1. Ahsan Adeel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jawad Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hadi Larijani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amir Hussain
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AA and AH conceived and developed the original idea reported in this paper, of integrating 5G, IoT, and lightweight encryption, with the lip-reading driven hearing-aid. AA and JA performed the simulations.

Corresponding author

Correspondence to Ahsan Adeel.

Ethics declarations

This manuscript has not been published in whole or in part elsewhere, which has also not currently being considered for publication in another journal. All authors have been personally and actively involved in substantive work leading to the manuscript, and will hold themselves jointly and individually responsible for its content.

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

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

Adeel, A., Ahmad, J., Larijani, H. et al. A Novel Real-Time, Lightweight Chaotic-Encryption Scheme for Next-Generation Audio-Visual Hearing Aids. Cogn Comput 12, 589–601 (2020). https://doi.org/10.1007/s12559-019-09653-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-019-09653-z

Keywords

Search

Navigation