CAPS：一种使用带带宽扩展的亚奈奎斯特采样实现可穿戴设备节能的级联重建模型
CAPS: A Cascaded Reconstruction Model to Power Saving in Hearables Using Sub-Nyquist Sampling with Bandwidth Extension
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中文总结 AI 辅助
研究可穿戴设备中联合降低ADC采样比特分辨率和频率对功耗及音频质量的影响，提出CAPS模型，采用亚奈奎斯特采样和低比特分辨率，降低功耗3.3倍，支持移动平台流操作，确保语音清晰度，平衡效率与节能。
中文摘要 AI 辅助
可穿戴设备是戴在耳朵上的可穿戴计算机。骨传导麦克风与空气传导麦克风一起用于可穿戴设备中，以在嘈杂环境中进行多模态语音增强。尽管有这种潜力，但当前模型在很大程度上未能探索在可穿戴设备的模数转换器（ADC）中联合降低采样比特分辨率和采样频率如何影响功耗和音频质量。此外，当前框架无法在可穿戴设备中进行亚奈奎斯特采样，因为它们缺乏从窄带分量重建宽带信号的方法。因此，我们提出了CAPS，它（i）在ADC中有意采用亚奈奎斯特采样和低比特分辨率，使可穿戴设备的功耗降低3.3倍，（ii）支持移动平台上的流操作，推理时间为1.36毫秒，内存占用为11.04MB。CAPS确保在现实环境中实现强大的语音清晰度，弥合了效率和节能之间的差距。
英文摘要
Hearables are wearable computers worn on the ear. Bone conduction microphones are used with air conduction microphones in hearables for multimodal speech enhancement in noisy conditions. Despite this potential, current models largely fail to explore how jointly reducing sampling bit resolution and sampling frequency in analog-to-digital converters (ADCs) of hearables impacts both power usage and audio quality. Furthermore, current frameworks cannot do sub-Nyquist sampling in hearables because they lack a method to reconstruct wideband signals from narrowband components. We therefore propose CAPS, which (i) intentionally employs sub-Nyquist sampling and low bit resolution in ADCs, achieving a 3.3x reduction in power consumption in hearables, and (ii) supports streaming operation on mobile platforms with an inference time of 1.36 ms and a memory footprint of 11.04 MB. CAPS ensures robust speech intelligibility in real-world settings, bridging the gap between efficiency and power savings.
发表机构
- George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。
