LAUSR

Various activities of construction equipment are associated with distinctive sound patterns (e.g., excavating soil, breaking rocks, etc.). Considering this fact, it is possible to extract useful information about construction operations by recording the audio at a jobsite and then processing this data to determine what activities are being performed. Audio-based analysis of construction operations mainly depends on specific hardware and software settings to achieve satisfactory performance. This paper explores the impacts of these settings on the ultimate performance on the task of interest. To achieve this goal, an audio-based system has been developed to recognize the routine sounds of construction machinery. The next step evaluates three types of microphones (off-the-shelf, contact, and a multichannel microphone array) and two installation settings (microphones placed in machines’ cabin and installed on the jobsite in relatively proximity to the machines). Two different jobsite conditions have been considered: (1) jobsites with single machines and (2) jobsites with multiple machines operating simultaneously. In terms of software settings, two different SVM classifiers (RBF and linear kernels) and two common frequency feature extraction techniques (STFT and CWT) were selected. Experimental data from several jobsites was gathered and the results depict an accuracy over 85% for the proposed audio-based recognition system. To better illustrate the practical value of the proposed system, a case study for calculating productivity rates of a sample piece of equipment is presented at the end.