  Sound source proximity and distance estimation are of great interest in many
practical applications, since they provide significant information for acoustic
scene analysis. As both tasks share complementary qualities, ensuring efficient
interaction between these two is crucial for a complete picture of an aural
environment. In this paper, we aim to investigate several ways of performing
joint proximity and direction estimation from binaural recordings, both defined
as coarse classification problems based on Deep Neural Networks (DNNs).
Considering the limitations of binaural audio, we propose two methods of
splitting the sphere into angular areas in order to obtain a set of directional
classes. For each method we study different model types to acquire information
about the direction-of-arrival (DoA). Finally, we propose various ways of
combining the proximity and direction estimation problems into a joint task
providing temporal information about the onsets and offsets of the appearing
sources. Experiments are performed for a synthetic reverberant binaural dataset
consisting of up to two overlapping sound events.
