A Study of Binaural Deep Beamforming With Interpretable Beampatterns Guided by Time-Varying RTF

Abstract: In this work, a deep beamforming framework for speech enhancement in dynamic acoustic environments is studied. The time-varying beamformer weights are estimated from the noisy multichannel signals by minimizing an SI-SDR loss. The estimation is guided by the continuously tracked relative transfer functions (RTFs) of the moving target speaker. The spatial behavior of the network is evaluated through both narrowband and wideband beampatterns under three settings: (i) oracle guidance using true RTFs, (ii) estimated RTFs obtained by a subspace tracking method, and (iii) without the RTF guidance. Results show that RTF-guided models produce smoother, spatially consistent beampatterns that accurately track the target's direction of arrival. In contrast, the model fails to maintain a clear spatial focus when guidance is absent. Using the estimated RTFs as guidance closely matches the oracle RTF behavior, confirming the effectiveness of the tracking scheme. The model also outputs a binaural signal to preserve the speaker's spatial cues, which promotes hearing aid and hearables applications.