A fundamental problem of every intermittently-powered sensing system is that signals acquired by these systems over a longer period in time are also intermittent. As a consequence, these systems fail to capture parts of a longer-duration event that spans over multiple charge-discharge cycles of the capacitor that stores the harvested energy. From an application's perspective, this is viewed as sporadic bursts of missing values in the input data -- which may not be recoverable using statistical interpolation or imputation methods. In this paper, we study this problem in the light of an intermittent audio classification system and design an end-to-end system -- SoundSieve -- that is capable of accurately classifying audio events that span multiple on-off cycles of the intermittent system. SoundSieve employs an offline audio analyzer that learns to identify and predict important segments of an audio clip that must be sampled to ensure accurate classification of the audio. At runtime, SoundSieve employs a lightweight, energy- and content-aware audio sampler that decides when the system should wake up to capture the next chunk of audio; and a lightweight, intermittence-aware audio classifier that performs imputation and on-device inference. Through extensive evaluations using popular audio datasets as well as real systems, we demonstrate that SoundSieve yields 5%--30% more accurate inference results than the state-of-the-art.

翻译：每个间歇性供电传感系统的一个基本问题是，这些系统在较长时间内采集的信号也是间歇性的。因此，这些系统无法捕捉跨越电容（存储收集的能量）多个充放电周期的长持续时间事件的完整片段。从应用角度看，这表现为输入数据中稀疏突发的缺失值，且无法通过统计插值或填补方法恢复。本文从间歇性音频分类系统的视角研究该问题，并设计了一个端到端系统——SoundSieve——能够准确分类跨越间歇系统多个开关周期的音频事件。SoundSieve采用离线音频分析器，学习识别并预测音频片段中必须采样以确保分类准确性的关键片段。运行时，SoundSieve配备轻量级、能量与内容感知的音频采样器，决定系统何时唤醒以捕获下一段音频；以及轻量级、间歇性感知的音频分类器，执行数据填补与设备端推理。通过使用主流音频数据集和真实系统进行的广泛评估，我们证明SoundSieve比现有最优方法产生5%–30%更精确的推理结果。