Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Bin Encoding Training of a Spiking Neural Network-based Voice Activity
  Detection   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.

31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph