Deployment of an IoT System for Adaptive In-Situ Soundscape Augmentation

TL;DR

This paper presents an IoT-based system that dynamically selects and reacts to soundscape changes in real-time for noise mitigation, leveraging cloud computing for scalable inference.

Contribution

It introduces a novel IoT and cloud-based architecture enabling real-time, adaptive soundscape augmentation without human supervision.

Findings

Prototype deployed in a high-traffic public area

Near real-time soundscape adaptation achieved

Scalable cloud inference system implemented

Abstract

Soundscape augmentation is an emerging approach for noise mitigation by introducing additional sounds known as "maskers" to increase acoustic comfort. Traditionally, the choice of maskers is often predicated on expert guidance or post-hoc analysis which can be time-consuming and sometimes arbitrary. Moreover, this often results in a static set of maskers that are inflexible to the dynamic nature of real-world acoustic environments. Overcoming the inflexibility of traditional soundscape augmentation is twofold. First, given a snapshot of a soundscape, the system must be able to select an optimal masker without human supervision. Second, the system must also be able to react to changes in the acoustic environment with near real-time latency. In this work, we harness the combined prowess of cloud computing and the Internet of Things (IoT) to allow in-situ listening and playback using microcontrollers while delegating computationally expensive inference tasks to the cloud. In particular, a serverless cloud architecture was used for inference, ensuring near real-time latency and scalability without the need to provision computing resources. A working prototype of the system is currently being deployed in a public area experiencing high traffic noise, as well as undergoing public evaluation for future improvements.