Unlike model-based direction of arrival (DoA) estimation algorithms,
supervised learning-based DoA estimation algorithms based on deep neural
networks (DNNs) are usually trained for one specific microphone array geometry,
resulting in poor performance when applied to a different array geometry. In
this paper we illustrate the fundamental difference between supervised
learning-based and model-based algorithms leading to this sensitivity. Aiming
at designing a supervised learning-based DoA estimation algorithm that
generalizes well to different array geometries, in this paper we propose a
geometry-aware DoA estimation algorithm. The algorithm uses a fully connected
DNN and takes mixed data as input features, namely the time lags maximizing the
generalized cross-correlation with phase transform and the microphone
coordinates, which are assumed to be known. Experimental results for a
reverberant scenario demonstrate the flexibility of the proposed algorithm
towards different array geometries and show that the proposed algorithm
outperforms model-based algorithms such as steered response power with phase
transform.Comment: Submitted to ICASSP 202