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

Soundscape augmentation is an emerging approach for noise mitigation by introducing additionalsounds known as "maskers" to increase indices like acoustic comfort. Traditionally, the choice of maskers is oftenpredicated on expert guidance or post-hoc analysis which can be time-consuming and sometimesarbitrary. Moreover, this often results in a static set of maskers that are inflexible to the dynamicnature of real-world acoustic environments. Overcoming the inflexibility of traditional soundscapeaugmentation is two-fold. First, given a snapshot of a soundscape, the system must be able to select anoptimal masker without human supervision. Second, the system must also be able to react to changesin the acoustic environment with near real-time latency. In this work, we harness the combinedprowess of cloud computing and internet-of-things (IoT) to allow in-situ listening and playback usingmicrocontrollers while delegating the computationally expensive inference tasks to the cloud. Inparticular, a serverless cloud architecture was used for inference, ensuring near real-time latencyand scalability without the need to provision computing resources. A working prototype of the systemis currently being deployed in a public area experiencing high traffic noise, as well as undergoingpublic evaluation for future improvements.