A 1μW Voice Activity Detector Using Analog Feature Extraction and Digital Deep Neural Network

📋 论文概要

该论文提出了一种采用模拟特征提取和数字深度神经网络的超低功耗语音活动检测器，总功耗仅1μW，解决了在能量收集和电池供电设备中实现自然语音交互的关键问题。通过结合模拟前端和数字推理，实现了高能效的语音/噪声区分。

💡 主要创新点

• 首次在语音活动检测器中结合模拟特征提取与深度神经网络，大幅降低功耗至1μW。
• 采用模拟电路实现特征提取（如滤波器、能量计算），避免传统数字特征提取的高功耗。
• 设计的数字深度神经网络仅需极少量参数，适合超低功耗推理引擎。

🏷 关键词

📄 原文摘要

Aurel A. Lazar, Mingoo Seok Columbia University, New York, NY Voice user interfaces (UIs) are highly compelling for wearable and mobile devices. They have the advantage of using compact and ultra-low-power (ULP) input devices (e.g. passive microphones). Together with ULP signal acquisition and processing, voice UIs can give energy-harvesting acoustic sensor nodes and battery-operating devices the sought-after capability of natural interaction with humans. Voice activity detection (VAD), separating speech from background noise, is a key building block in such voice UIs, e.g. it can enable power gating of higher-level speech tasks such as speaker identification and speech recognition

👥 作者与机构

Minhao Yang, Chung-Heng Yeh, Yiyin Zhou, Joao P. Cerqueira,