High-Performance, Low Complexity Yelp Siren Detection System

Abstract

Effective detection of emergency vehicle sirens is crucial for accident prevention and road safety, particularly at intersections where visual detection may be insufficient. This study addresses the challenges faced by existing siren detection systems, which often involve complex signal processing and high computational demands. A streamlined detection system is presented, designed specifically for recognizing yelp sirens, one of the most prevalent siren sounds. This approach, inspired by analog electronics, offers a cost-effective and low-complexity solution suitable for deployment in either digital or predominantly analog setups. Evaluation using a comprehensive dataset of real-world siren signals and road noise demonstrates performance comparable to state-of-the-art methods while significantly reducing computational and energy requirements. Additionally, a thorough analysis of operational parameters is included to optimize performance and cost-effectiveness. All data and methods are publicly accessible to ensure transparency and reproducibility. This work enhances road safety and accessibility for hearing-impaired drivers and provides a foundation for future research on expanding detection capabilities to other siren types.