DEVELOPMENT OF A SOUND-BASED MOBILE APPLICATION FOR ROAD ACCIDENT DETECTION USING MACHINE LEARNING AND SPECTROGRAM ANALYSIS

Abstract

Road accidents continue to pose a serious threat to public safety, underscoring the need for innovative, automated emergency response systems. This study presents the development of a mobile application that detects road accidents by analyzing audio signals in real time and immediately sends SMS alerts with GPS coordinates to emergency services and user-specified contacts. The system comprises two parts: a user-facing Android application and a server-side component for data processing. To build and train the detection models, we leverage the MIVIA Road Audio Events dataset and applied preprocessing techniques including amplitude normalization, background noise filtering, and data augmentation. Feature extraction involved zero-crossing rate, spectral centroid, spectral flux, energy entropy, short-time Fourier transform (STFT), and Mel-frequency cepstral coefficients (MFCCs). Two classification approaches were investigated: traditional machine learning models (Support Vector Machine, Random Forest, Gradient Boosting) and a deep learning model based on convolutional neural networks (CNNs) using Mel spectrogram inputs. Experimental results demonstrate that the CNN model achieved the highest performance with 91.2% accuracy, 89.5% recall, and an F1-score of 90.3%, outperforming the best classical model (Random Forest), which achieved 85.1% accuracy. The system also reduced the average accident alert time from 5–7 minutes to 1–2 minutes, representing a 60–80% improvement in emergency response speed. These results confirm the system’s reliability and practical benefit, particularly in regions like Kazakhstan, where timely medical intervention is critical. Limitations include reliance on smartphone availability, internet access, and environmental sound conditions. Future work will explore real-world testing, integration of accelerometer and gyroscope data, and deployment of edge computing for faster on-device processing. Overall, the proposed solution is a cost-effective, scalable approach for improving road safety and saving lives through rapid, automated accident detection.