A Software-Based Approach for Acoustical Modeling of Construction Job Sites with Multiple Operational Machines

Several studies have been conducted to automatically recognize activities of construction equipment using their generated sound patterns. Most of these studies are focused on single-machine scenarios under controlled environments. However, real construction job sites are more complex and often consist of several types of equipment with different orientations, directions, and locations working simultaneously. The current state-of-research for recognizing activities of multiple machines on a job site is hardware-oriented, on the basis of using microphone arrays (i.e., several single microphones installed on a board under specific geometric layout) and beamforming principles for classifying sound directions for each machine. While effective, the common hardware-approach has limitations and using microphone arrays is not always a feasible option at ordinary job sites. In this paper, the authors proposed a software-oriented approach using Deep Neural Networks (DNNs) and Time-Frequency Masks (TFMs) to address this issue. The proposed method requires using single microphones, as the sound sources could be differentiated by training a DNN. The presented approach has been tested and validated under simulated job site conditions where two machines operated simultaneously. Results show that the average accuracy for soft TFM is 38% higher than binary TFM.