  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.
