Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Environmental Sound Classification on the Edge: A Pipeline for Deep
  Acoustic Networks on Extremely Resource-Constrained Devices   Significant efforts are being invested to bring state-of-the-art
classification and recognition to edge devices with extreme resource
constraints (memory, speed and lack of GPU support). Here, we demonstrate the
first deep network for acoustic recognition that is small enough for an
off-the-shelf microcrocontroller, yet achieves state-of-the-art performance on
standard benchmarks. Rather than handcrafting a once-off solution, we present a
universal pipeline that converts a large deep convolutional network
automatically via compression and quantization into a network for
resource-impoverished edge devices. After introducing ACDNet, which produces
above state-of-the-art accuracy on ESC-10 (96.65%) and ESC-50 (87.1%), we
describe the compression pipeline and show that it allows us to achieve 97.22%
size reduction and 97.28% FLOP reduction while maintaining close to
state-of-the-art accuracy (83.65% on ESC-50). We describe a successful
implementation on a standard off-the-shelf microcontroller and, beyond
laboratory benchmarks, report successful tests on real-world data sets.

11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom