Excavation Devices Classification Using Enhanced Acoustics by MVDR Beamforming With a Cross Microphone Array

Acoustic signal based recognition for excavation devices has been investigated in the past due to its significance in preventing the underground cables from being destroyed during the ground excavation. However, existing excavation devices classification algorithms have paid little attention to the reduction of background noises which usually severely degrade the recognition performance. This paper utilizes a cross microphone array to record the acoustic signals of excavation devices, which are then filtered by the minimum variance distortionless response (MVDR) beamforming algorithm to reduce the environment noises and enhance the desired signals. The filtered signals are then fed into the feature extraction and classifier learning. To show the effectiveness of the proposed method, we collected the real acoustics of two representative devices in a construction site to make the performance testing. Experiments show that, compared with conventional recognition methods the performance of the proposed method is significantly improved.