DesPatNet25: Data encryption standard cipher model for accurate automated construction site monitoring with sound signals

Nowadays, various construction site monitoring (CSM) models have been presented using sound signals. Many researchers have used deep learning (DL) networks to develop an accurate automated CSM model. These DL-based models require huge dataset to train the model and also such networks are complex. Hence, in this work, a novel hand-modeled automated system is developed using a public CSM sound dataset. The proposed model uses the first S-Box of the data encryption standard (DES) cipher as a feature generator by using two binary kernels. Using tent average pooling, sub-bands (compressed) sound signals are generated and the presented multiple kernelled DES pattern generates features from each signal. The proposed hand-modeled automated system extracts 25 feature vectors, hence it is named as DesPatNet25. The developed DesPatNet25 consists of: (i) feature vectors creation, (ii) feature selection using iterative neighborhood component analysis (INCA), and (iii) classification. Our proposed model attained accuracies of 96.77% and 97.05% using k-nearest neighbor (kNN) classifier with 10-fold cross-validation and hold-out validation (80:20 split ratio) techniques, respectively. These high classification accuracies clearly demonstrate the success of the DesPatNet25 model with sound signal classification for automated CSM tasks.