End-to-End Signal-Aware Direction-of-Arrival Estimation Using Weighted Steered-Response Power

The direction-of-arrival (DOA) of acoustic sources is an important parameter used in multichannel acoustic signal processing to perform, e.g., source extraction. Deep learning-based time-frequency masking has been widely used to make DOA estimators signal-aware, i.e., to localize only the sources of interest (SOIs) and disregard other sources. The mask is applied to feature representations of the microphone signals. DOA estimators can either be model-based or deep learning-based, such that the combination with the deep learning-based masking estimator can either be hybrid or fully data-driven. Although fully data-driven systems can be trained end-to-end, existing training losses for hybrid systems like weighted steered-response power require ground-truth microphone signals, i.e., signals containing only the SOIs. In this work, we propose a loss function that enables training hybrid DOA estimation systems end-to-end using the noisy microphone signals and the ground-truth DOAs of the SOIs, and hence does not dependent on the ground-truth signals. We show that weighted steered-response power trained using the proposed loss performs on par with weighted steered-response power trained using an existing loss that depends on the ground-truth microphone signals. End-to-end training yields consistent performance irrespective of the explicit application of phase transform weighting.