  Outdoor acoustic events detection is an exciting research field but
challenged by the need for complex algorithms and deep learning techniques,
typically requiring many computational, memory, and energy resources. This
challenge discourages IoT implementation, where an efficient use of resources
is required. However, current embedded technologies and microcontrollers have
increased their capabilities without penalizing energy efficiency. This paper
addresses the application of sound event detection at the edge, by optimizing
deep learning techniques on resource-constrained embedded platforms for the
IoT. The contribution is two-fold: firstly, a two-stage student-teacher
approach is presented to make state-of-the-art neural networks for sound event
detection fit on current microcontrollers; secondly, we test our approach on an
ARM Cortex M4, particularly focusing on issues related to 8-bits quantization.
Our embedded implementation can achieve 68% accuracy in recognition on
Urbansound8k, not far from state-of-the-art performance, with an inference time
of 125 ms for each second of the audio stream, and power consumption of 5.5 mW
in just 34.3 kB of RAM.
