Beamforming Alternatives for Multi-Channel Transient Acoustic Event Classification

Signals acquired through a microphone array are typically beamformed to combine channels and improve the signal-to-noise ratio (SNR). However, it has been previously shown that alternative methods for handling multi-channel systems can outperform beamforming for speech recognition applications. In this paper, we implemented a comprehensive set of classification tests using multiple classifiers and feature extraction techniques to determine whether the alternative methods generalize beyond speech recognition applications. We show that applying the alternative methods (in a slightly simpler form) outperforms beamforming when used for classifying transient acoustic projectile weapon signals. Furthermore, an additional technique is introduced which outperforms both beamforming and previously proposed alternatives in certain classification scenarios. For the majority of classification tests, the improvements seen through the use of these alternative methods are statistically significant.