  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, flexible and
compression-friendly yet achieves state-of-the-art performance for raw audio
classification. Rather than handcrafting a once-off solution, we present a
generic pipeline that automatically converts a large deep convolutional network
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%), ESC-50 (87.10%), UrbanSound8K (84.45%) and
AudioEvent (92.57%), 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 96.25%, 83.65%, 78.27% and
89.69% on these datasets. We describe a successful implementation on a standard
off-the-shelf microcontroller and, beyond laboratory benchmarks, report
successful tests on real-world datasets.
