LAUSR

The main challenging goals in acoustic modality based moving vehicle recognition system is to accurately classify the moving vehicle with minimum misclassification rate. This article proposes an acoustic modality-based hybrid deep 1D convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) technique for moving vehicle classification under two-wheeler, low, medium, heavy weight category and noise. The proposed algorithm automatically extracts the high-level feature sequentially from experimentally generated vehicles signal and hold these features into the network for analyzing the time-varying characteristic for classification. Additionally, it is tested on the reference dataset SITEX02 for validation with 96% accuracy. The performance of 1D CNN-BiLSTM model has been compared with the conventional classifiers i.e., SVM, ANN, CNN and CNN-LSTM models. The experimental results show that CNN-BiLSTM has attained higher classification accuracy, 0.92 and minimum misclassification rate, 0.08 as compared to conventional classifiers. It has not only achieved satisfactory performance measures values and ROC-AUC characteristics but also obtained better generalization ability along with stability on the learning curve.