Signal-Aware Broadband DOA Estimation Using Attention Mechanisms

Abstract

We refer to direction-of-arrivals (DOAs) estimation of a user-defined subset of directional (desired) sound sources as signal-aware DOA estimation. Source selection, thereby, can be achieved with time-frequency masks to apply attention to TF bins dominated by desired sources. With deep neural networks (DNNs), another option is to train the DNN to estimate the DOAs only of specific classes, like speech, and disregard the DOAs of other classes. Consequently, changing the desired classes requires retraining the DNN. Also, the mask-based approaches are trained for sources known prior to DNN training. To obtain a flexible signal-aware DOA estimator, we propose to use binary mask attention with a DNN for multi-source DOA estimation trained with artificial noise. The desired sources are determined via binary masks, which allows a redefinition by changing the masks. Consequently, the DOA estimator is independent of the desired sources. We experiment with attention in form of oracle and estimated binary masks.