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.

中文翻译：

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一种用于下一代视听助听器的新型实时，轻量级混沌加密方案

下一代视听（AV）助听器是实现更清晰音频的主要推动力。但是，高数据速率，低延迟，低计算复杂度和隐私性是成功部署此类高级助听器的主要瓶颈。为了应对这些挑战，我们建议将5G云无线电接入网络（C-RAN），物联网（IoT）和强大的隐私算法集成在一起，以充分利用这些技术所提供的可能性。已知现有的仅音频助听器在存在压倒性噪声的嘈杂情况下表现不佳。当前的设备使信号更容易听见，但在恢复清晰度方面仍然不足。因此，需要助听器，其可以选择性地放大参与的讲话者或滤除声波杂波。拟议中的支持5G IoT的AV助听框架可传输加密的压缩AV信息，并实时接收加密的增强型重构语音，以应对网络安全攻击，例如位置隐私和窃听。为了实现安全性，提出了一种基于分段线性混沌映射（PWLSM），切比雪夫映射以及安全哈希和S-Box算法的实时轻量级AV加密。对于语音增强，云中接收到的安全AV（包括唇读）信息用于通过深度学习和分析声学建模来过滤嘈杂的音频。为了减轻计算复杂性和实时优化问题，该框架在后台在云上运行深度学习和大数据优化过程。使用广为人知的安全指标广泛评估了提议的支持5G-IoT的AV助听器框架的有效性和安全性。我们使用基准Grid和ChiME3语料库，在四种不同的动态现实世界场景（咖啡馆，街道，公共交通，行人专用区）下评估了我们最新报道的深度学习驱动的唇读法，用于语音增强。在语音增强和AV加密方面进行的比较关键性分析表明，所构想的技术具有交付高质量语音重建和安全移动AV助听器通信的潜力。我们认为，我们提出的支持5G IoT的AV助听器框架是一种有效且可行的解决方案，代表了下一代多模式数字助听器开发中的一步变化。