  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.
