# Infrastructure-free, Deep Learned Urban Noise Monitoring at ~100mW

arXiv: https://arxiv.org/abs/2203.06220 · HTML: https://mlsanity.com/paper/2203.06220/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2022.

## Built on by (most cited first, 3 of 3)

- [LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement](https://mlsanity.com/paper/2407.21204/) (2024)
- [PAMLR: A Passive-Active Multi-Armed Bandit-Based Solution for LoRa Channel Allocation](https://mlsanity.com/paper/2410.05147/) (2024)
- [MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification](https://mlsanity.com/paper/2506.11331/) (2025)

## Abstract (arXiv)

The Sounds of New York City (SONYC) wireless sensor network (WSN) has been fielded in Manhattan and Brooklyn over the past five years, as part of a larger human-in-the-loop cyber-physical control system for monitoring, analyzing, and mitigating urban noise pollution. We describe the evolution of the 2-tier SONYC WSN from an acoustic data collection fabric into a 3-tier in situ noise complaint monitoring WSN, and its current evaluation. The added tier consists of long-range (LoRa), multi-hop networks of a new low-power acoustic mote, MKII ("Mach 2"), that we have designed and fabricated. MKII motes are notable in three ways: First, they advance machine learning capability at mote-scale in this application domain by introducing a real-time Convolutional Neural Network (CNN) based embedding model that is competitive with alternatives while also requiring 10× lesser training data and ~2 orders of magnitude fewer runtime resources. Second, they are conveniently deployed relatively far from higher-tier base station nodes without assuming power or network infrastructure support at operationally relevant sites (such as construction zones), yielding a relatively low-cost solution. And third, their networking is frequency agile, unlike conventional LoRa networks: it tolerates in a distributed, self-stabilizing way the variable external interference and link fading in the cluttered 902-928MHz ISM band urban environment by dynamically choosing good frequencies using an efficient new method that combines passive and active measurements.