Design of a Configurable Acoustic Sensor Network for Privacy-Compliant Urban Soundscape Recordings

Abstract

Environmental acoustics, particularly urban soundscape monitoring, has gained increasing consideration since the United Nations established the Sustainable Development Goals in 2015, and to an even greater extent with the rise of privacy concerns following the introduction of global regulations such as GDPR. As a result, privacy-compliant devices have become essential for soundscape monitoring in urban environments. In this paper, we present the design and implementation of a portable AI-driven, privacy-compliant urban sound recording device that locally captures and processes acoustic data on the edge. In more detail, this device operates as a sensor that captures soundscapes and processes them through a pipeline, which employs a pre-trained open-source AI model to anonymize human voices, ensuring privacy without compromising the integrity of the acoustic environment. The anonymization process alters human speech in a way that protects identity while maintaining environmental audio quality. The device can function as a standalone sensor or as part of a synchronized network of distributed sensors. Privacy-focused evaluation of the device's recordings indicates that, while the anonymization process impacts speech intelligibility, it preserves the overall soundscape with a recall rate of 96%. The system was deployed in a real-world setting with four temporally synchronized sensors. While network synchronization was achieved, a 1 to 2-s deviation was occasionally observed in the first duty cycle interval, reflecting timing variability inherent to Cron-based script triggering. This limitation has been identified for future refinement. This work demonstrates the feasibility of deploying privacy-compliant, edge-based soundscape sensors in urban environments, contributing to privacy preservation, and enhanced public safety.