  We investigate the objective performance of five high-end commercially
available Hearing Aid (HA) devices compared to DNN-based speech enhancement
algorithms in complex acoustic environments. To this end, we measure the HRTFs
of a single HA device to synthesize a binaural dataset for training two
state-of-the-art causal and non-causal DNN enhancement models. We then generate
an evaluation set of realistic speech-in-noise situations using an Ambisonics
loudspeaker setup and record with a KU100 dummy head wearing each of the HA
devices, both with and without the conventional HA algorithms, applying the DNN
enhancers to the latter. We find that the DNN-based enhancement outperforms the
HA algorithms in terms of noise suppression and objective intelligibility
metrics.

Sound;Audio and Speech Processing