Relative Transfer Matrix for Drone Audition Applications: Source Enhancement

The Relative Transfer Matrix (ReTM), recently introduced as a generalization of the relative transfer function for multiple sources and multiple microphones, shows promising performance when applied to speech denoising and speaker separation in a noisy reverberant room. This work utilizes the ReTM to propose a novel framework for drone noise suppression. Difficulties in noise cancellation for drone audition applications arise due to its self-generated noise that causes an adverse noisy environment of low Signal-to-Drone Noise Ratio (SDNR) levels. In this paper, we divide the drone on-board microphones into two multichannel groups to approximately estimate the drone noise from one group to the other with known drone noise ReTM for denoising. We demonstrate the ReTM spatial mapping ability in both indoor and outdoor experiments using hovering drone on-board microphone recordings with low average magnitude spectrum error. Finally, we validate the method in a real-life environment for source signal enhancement over different ${\text{S}}\bar {\text{DNR}}$ conditions and offer both improved speech intelligibility and signal-to-distortion ratio.