Detecting emergency vehicles With 1D-CNN using fourier processed audio signals

Abstract

Emergency Vehicle Detection (EVD) is a critical task in transportation management. Vehicle audio analysis provides affordable and insightful data, compared to established yet expensive vehicle detection methods. This research presents two contributions. Firstly, to decrease the processing power and memory demands, it offers a method to detect the optimal maximum sampling frequencies through an analysis of correlation between spectral entropy (SE) and dataset variance, grounded in the principles of the discrete Fourier transform (DFT) and Nyquist–Shannon theorem. Secondly, to address the inconsistency between recorded data in different datasets, this paper proposes segmenting audio into small input time windows (ITW) and applying individualized normalization. Training the model on dataset-1 achieves 98.37% accuracy on the validation set, with 86.83% accuracy on dataset-2. Comparative analysis with the baseline Long-Short-Term Memory model shows 22.79% performance improvement in favor of the proposed 1D-CNN model. Overall, this model outperforms state-of-the-art techniques, achieving accuracy rate of 98.37%.