A Denoising Preprocessing Framework via Orthogonal Multi-Tap Null-Steering Beamformer Bank: Facilitating Target Signal Preservation Under Low SINR Conditions and Complex Soundscapes

Acoustic indices are popular tools for rapid biodiversity assessment using passive acoustic monitoring recordings, yet anthropogenic sounds in human activity areas compromise their robustness. In this paper, we focus on the typical urban–rural soundscape, where anthropogenic noise mainly originates from a narrow angular sector far from the monitoring device. We propose a denoising preprocessing algorithm with two microphone sensors for the robust application of existing acoustic indices. Our algorithm first develops an adaptive multi-tap null-steering beamformer based on a back-to-back first-order differential microphone array, which increases the system degrees of freedom to enhance the broadband interference cancellation capability. Building on this, a parallel bank of mutually orthogonal null-steering beamformers is proposed, each forming deep nulls toward directional interference-concentrated bands and generating diverse responses to the target signal. Finally, a signal compensation mechanism is applied to the beamformers’ outputs, mitigating the signal self-cancellation effects from these unconstrained adaptive beamformers prior to index calculation. The proposed preprocessing method is evaluated using the frequency-dependent acoustic diversity index as a representative of acoustic indices. Experiment results on both simulation and real-world recordings show that the proposed method generates high-fidelity acoustic information for subsequent acoustic index calculation over a much wider signal-to-interference-plus-noise ratio (SINR) range in urban–rural soundscapes characterized by directional anthropogenic interference.