DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection

TL;DR

This paper introduces federated learning datasets for sound event detection in domestic and urban environments, providing a foundation for privacy-preserving research and baseline results for future studies.

Contribution

It creates and publishes the first federated learning datasets for sound event detection and provides baseline results for three neural network architectures.

Findings

Federated learning shows promise for sound event detection.

Data distribution divergence poses challenges in FL for SED.

Baseline performance metrics are established for future research.

Abstract

Research on sound event detection (SED) in environmental settings has seen increased attention in recent years. The large amounts of (private) domestic or urban audio data needed raise significant logistical and privacy concerns. The inherently distributed nature of these tasks, make federated learning (FL) a promising approach to take advantage of largescale data while mitigating privacy issues. While FL has also seen increased attention recently, to the best of our knowledge there is no research towards FL for SED. To address this gap and foster further research in this field, we create and publish novel FL datasets for SED in domestic and urban environments. Furthermore, we provide baseline results on the datasets in a FL context for three deep neural network architectures. The results indicate that FL is a promising approach for SED, but faces challenges with divergent data distributions inherent to distributed client edge devices.