RT book part
T1 Explainable Artificial Intelligence for Audio-based Detection of Emergency Vehicles
A1 Balderas Díaz, Sara
A1 Guerrero Contreras, Gabriel José
A1 Muñoz Ortega, Andrés
A1 Durães, Dalila
A1 Novais, Paulo
A2 Ingeniería Informática
K1 Explainable Artificial Intelligence (XAI)
K1 audio-based emergency detection
K1 urban sound classification
K1 machine learning
K1 feature importance analysis
AB With the increasing adoption of AI in safety-critical applications within urban environments, the interpretability of these systems is paramount. This study explores the application of Explainable Artificial Intelligence (XAI) techniques to enhance transparency in audio-based detection of emergency vehicle sirens, a crucial component in urban sound management. Adopting methods such as SHAP (SHapley Additive exPlanations) values, Permutation Feature Importance, and model-specific feature scores, this research identifies key audio features, including mid-frequency spectral contrasts and targeted chroma components, which significantly help in distinguishing siren sounds among urban noise. The study examines various machine learning models, identifying K-Nearest Neighbors (KNN) and XGBoost as top performers; KNN excelled in class-specific precision, while XGBoost demonstrated strong cross-class discrimination. The findings highlight the potential of XAI in improving both accuracy and accountability for sound detection systems in safety-critical urban applications, advancing the deployment of transparent AI within smart city infrastructures.
PB IEEE Xplore
SN 979-8-3315-2358-9
SN 2472-7571
YR 2025
FD 2025-08-26
LK http://hdl.handle.net/10498/37705
UL http://hdl.handle.net/10498/37705
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026