Acoustic Based Emergency Vehicle Detection Using Ensemble of deep Learning Models

Abstract

The temporal and spectral structure is possessed in the time-frequency domain by sound events. Analyzing and classifying acoustic environment using sound recording is an emerging research area. Convolutional layers can quickly extract high-level features and shift-invariant features from the time-frequency domain. In this work, emergency vehicle detection (EVD) like fire brigades, ambulances, and police cars is done based upon their siren sounds. Dataset from Google Audioset ontology was collected and features are extracted by Mel-frequency Cepstral Coefficient (MFCC). Three deep neural networks (DNN) models (dense layer, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)) with different configurations and parameters have been investigated. Then, an ensemble model has been designed with optimum selected models by performing experimental tests on various configurations with hyper-parameter tuning. The proposed ensemble model provides the highest accuracy of 98.7%, while the recurrent neural network (RNN) model provides an accuracy of 94.5%. Also, performance analysis of deep learning models is done with various machine learning models like Perceptron, SVM, decision tree etc.