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Unlike model-based direction of arrival (DoA) estimation algorithms,supervised learning-based DoA estimation algorithms based on deep neuralnetworks (DNNs) are usually trained for one specific microphone array geometry,resulting in poor performance when applied to a different array geometry. Inthis paper we illustrate the fundamental difference between supervisedlearning-based and model-based algorithms leading to this sensitivity. Aimingat designing a supervised learning-based DoA estimation algorithm thatgeneralizes well to different array geometries, in this paper we propose ageometry-aware DoA estimation algorithm. The algorithm uses a fully connectedDNN and takes mixed data as input features, namely the time lags maximizing thegeneralized cross-correlation with phase transform and the microphonecoordinates, which are assumed to be known. Experimental results for areverberant scenario demonstrate the flexibility of the proposed algorithmtowards different array geometries and show that the proposed algorithmoutperforms model-based algorithms such as steered response power with phasetransform.

Geometry-aware DoA Estimation using a Deep Neural Network with mixed-data input features