A novel real-time, lightweight chaotic-encryption scheme for next-generation audio-visual hearing aids

Ahsan Adeel*, Jawad Ahmad, Hadi Larijani, Amir Hussain

*Corresponding author for this work

Research output: Contribution to journalArticle

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
Original languageEnglish
Pages (from-to)589-601
Number of pages13
JournalCognitive Computation
Volume12
Early online date13 Nov 2019
DOIs
Publication statusPublished - May 2020

Keywords

  • hearing aid
  • 5G Cloud-Radio Access Network
  • Internet of Things
  • cybersecurity
  • speech enhancement
  • deep learning

Fingerprint Dive into the research topics of 'A novel real-time, lightweight chaotic-encryption scheme for next-generation audio-visual hearing aids'. Together they form a unique fingerprint.

  • Cite this