Real-Time Emergency Vehicle Siren Detection with Efficient CNNs on Embedded Hardware

TL;DR

This paper introduces a real-time emergency vehicle siren detection system using optimized CNNs on embedded hardware, featuring curated datasets and a full-stack deployment for smart city applications.

Contribution

The work presents a novel CNN-based siren detection system optimized for embedded hardware, along with curated datasets and a full-stack implementation for real-time urban deployment.

Findings

Achieves low-latency, robust siren detection in urban environments.

Demonstrates effective deployment on Raspberry Pi 5 with real-time monitoring.

Shows potential for distributed acoustic monitoring in smart cities.

Abstract

We present a full-stack emergency vehicle (EV) siren detection system designed for real-time deployment on embedded hardware. The proposed approach is based on E2PANNs, a fine-tuned convolutional neural network derived from EPANNs, and optimized for binary sound event detection under urban acoustic conditions. A key contribution is the creation of curated and semantically structured datasets - AudioSet-EV, AudioSet-EV Augmented, and Unified-EV - developed using a custom AudioSet-Tools framework to overcome the low reliability of standard AudioSet annotations. The system is deployed on a Raspberry Pi 5 equipped with a high-fidelity DAC+microphone board, implementing a multithreaded inference engine with adaptive frame sizing, probability smoothing, and a decision-state machine to control false positive activations. A remote WebSocket interface provides real-time monitoring and facilitates live demonstration capabilities. Performance is evaluated using both framewise and event-based metrics across multiple configurations. Results show the system achieves low-latency detection with improved robustness under realistic audio conditions. This work demonstrates the feasibility of deploying IoS-compatible SED solutions that can form distributed acoustic monitoring networks, enabling collaborative emergency vehicle tracking across smart city infrastructures through WebSocket connectivity on low-cost edge devices.