Comparison of Different Beamforming-Based Approaches for Sound Source Separation of Multiple Heavy Equipment at Construction Job Sites

Abstract

Construction equipment performance monitoring can support detecting equipment idle time, estimating equipment productivity rates, and evaluating the cycle time of activities. Each equipment generates unique sound patterns that can be used for equipment activity detection. In the last decade, several audio-based methods are introduced to automate the process of equipment activity recognition. Most of these methods only consider single-equipment scenarios. The real construction job site consists of multiple machines working simultaneously. Thus, there is an increasing demand for advanced techniques to separate different equipment sound sources and evaluate each equipment’s productivity separately. In this study, six beamforming-based approaches for construction equipment sound source separation are implemented and evaluated using real construction job site data. The results show that Frost beamformer and time-delay Linear Constraint Minimum Variance (LCMV) generate outputs with array gains of more than 4.0, which are more reliable than the other four beamforming techniques for equipment sound separation.