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Sound source proximity and distance estimation are of great interest in manypractical applications, since they provide significant information for acousticscene analysis. As both tasks share complementary qualities, ensuring efficientinteraction between these two is crucial for a complete picture of an auralenvironment. In this paper, we aim to investigate several ways of performingjoint proximity and direction estimation from binaural recordings, both definedas coarse classification problems based on Deep Neural Networks (DNNs).Considering the limitations of binaural audio, we propose two methods ofsplitting the sphere into angular areas in order to obtain a set of directionalclasses. For each method we study different model types to acquire informationabout the direction-of-arrival (DoA). Finally, we propose various ways ofcombining the proximity and direction estimation problems into a joint taskproviding temporal information about the onsets and offsets of the appearingsources. Experiments are performed for a synthetic reverberant binaural datasetconsisting of up to two overlapping sound events.

Joint Direction and Proximity Classification of Overlapping Sound Events from Binaural Audio