Vehicle Sound Recognition Assistance in IoT Systems for Hearing-Impaired Drivers

Hearing-impaired drivers face significant challenges in detecting critical auditory cues, such as emergency vehicle sirens, essential for safe driving. This article presents an advanced IoT-based sound recognition system designed to enhance situational awareness for these drivers. Audible signals are recognized and transformed into alerts displayed in the dashboard. Our approach involves preprocessing audio data to extract 23 features. We normalize these features and evaluate multiple Machine Learning and Deep Learning models for their classification performance. The top five models, selected based on their performance metrics, are then combined into an ensemble model using majority voting to improve accuracy and robustness. Our dataset comprising 1500 audio samples enabled us to achieve a final accuracy of 94.2% with the ensemble voting approach. These results demonstrate a significant performance in sound classification accuracy compared to individual models, indicating the effectiveness of our ensemble approach. This research provides a valuable step towards developing more accessible and safer driving assistance systems for individuals with hearing impairments.