  Hearing aids are typically equipped with multiple microphones to exploit
spatial information for source localisation and speech enhancement. Especially
for hearing aids, a good source localisation is important: it not only guides
source separation methods but can also be used to enhance spatial cues,
increasing user-awareness of important events in their surroundings. We use a
state-of-the-art deep neural network (DNN) to perform binaural
direction-of-arrival (DoA) estimation, where the DNN uses information from all
microphones at both ears. However, hearing aids have limited bandwidth to
exchange this data. Bluetooth low-energy (BLE) is emerging as an attractive
option to facilitate such data exchange, with the LC3plus codec offering
several bitrate and latency trade-off possibilities. In this paper, we
investigate the effect of such lossy codecs on localisation accuracy.
Specifically, we consider two conditions: processing at one ear vs processing
at a central point, which influences the number of channels that need to be
encoded. Performance is benchmarked against a baseline that allows full
audio-exchange - yielding valuable insights into the usage of DNNs under lossy
encoding. We also extend the Pyroomacoustics library to include hearing-device
and head-related transfer functions (HD-HRTFs) to suitably train the networks.
This can also benefit other researchers in the field.
