# Compact recurrent neural networks for acoustic event detection on low-energy low-complexity platforms

arXiv: https://arxiv.org/abs/2001.10876 · HTML: https://mlsanity.com/paper/2001.10876/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 6 of 6)

- [Sound Event Detection with Binary Neural Networks on Tightly Power-Constrained IoT Devices](https://mlsanity.com/paper/2101.04446/) (2021)
- [Sub-mW Keyword Spotting on an MCU: Analog Binary Feature Extraction and Binary Neural Networks](https://mlsanity.com/paper/2201.03386/) (2022)
- [Training dynamic models using early exits for automatic speech recognition on resource-constrained devices](https://mlsanity.com/paper/2309.09546/) (2023)
- [An Open-Source Tool for Classification Models in Resource-Constrained Hardware](https://mlsanity.com/paper/2105.05983/) (2021)
- [DiTMoS: Delving into Diverse Tiny-Model Selection on Microcontrollers](https://mlsanity.com/paper/2403.09035/) (2024)
- [TinyML for Acoustic Anomaly Detection in IoT Sensor Networks](https://mlsanity.com/paper/2603.26135/) (2026)

## Abstract (arXiv)

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.