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dc.contributor.authorAdeel, Ahsan
dc.contributor.authorAhmad, Jawad
dc.contributor.authorLarijani, Hadi
dc.contributor.authorHussain, Amir
dc.identifier.citationAhsan, A., Ahmad, J., Larijani, H. and Hussain, A. (2019) A novel real-time, lightweight chaotic-encryption scheme to enable next-generation audio-visual hearing-aids, Cognitive Computation, 12, pp. 589–601.
dc.descriptionThis is an accepted manuscript of an article published by Elsevier in Cognitive Computation on 10/05/2019, available online: The accepted version of the publication may differ from the final published version.
dc.description.abstractObjective: Next-generation audiovisual (AV) hearing-aids stand as a major enabler to realise 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 a novel framework based on 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. Background: 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 remains deficient to restore intelligibility. Thus, we need hearing aids that can selectively amplify the attended talker or filter out acoustic clutter Methods: 1 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. Results: The effectiveness and security of our 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 demonstrate the potential of our envisioned technology to deliver high quality speech reconstruction and secure mobile AV hearing aid communication. Conclusion: We believe that the proposed 5G IoT enabled AV hearing aid 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.en
dc.publisherSpringer Natureen
dc.subjectHearing Aiden
dc.subject5G Cloud-Radio Access Networken
dc.subjectDeep Learningen
dc.subjectInternet of thingsen
dc.titleA novel real-time, lightweight chaotic-encryption scheme for next-generation audio-visual hearing-aidsen
dc.typeJournal articleen
dc.identifier.journalCognitive Computationen
rioxxterms.funderEngineering and Physical Sciences Research Councilen

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