  Advances of deep learning for Artificial Neural Networks(ANNs) have led to
significant improvements in the performance of digital signal processing
systems implemented on digital chips. Although recent progress in low-power
chips is remarkable, neuromorphic chips that run Spiking Neural Networks (SNNs)
based applications offer an even lower power consumption, as a consequence of
the ensuing sparse spike-based coding scheme. In this work, we develop a
SNN-based Voice Activity Detection (VAD) system that belongs to the building
blocks of any audio and speech processing system. We propose to use the bin
encoding, a novel method to convert log mel filterbank bins of single-time
frames into spike patterns. We integrate the proposed scheme in a bilayer
spiking architecture which was evaluated on the QUT-NOISE-TIMIT corpus. Our
approach shows that SNNs enable an ultra low-power implementation of a VAD
classifier that consumes only 3.8$\mu$W, while achieving state-of-the-art
performance.
