  Almost half a billion people world-wide suffer from disabling hearing loss.
While hearing aids can partially compensate for this, a large proportion of
users struggle to understand speech in situations with background noise. Here,
we present a deep learning-based algorithm that selectively suppresses noise
while maintaining speech signals. The algorithm restores speech intelligibility
for hearing aid users to the level of control subjects with normal hearing. It
consists of a deep network that is trained on a large custom database of noisy
speech signals and is further optimized by a neural architecture search, using
a novel deep learning-based metric for speech intelligibility. The network
achieves state-of-the-art denoising on a range of human-graded assessments,
generalizes across different noise categories and - in contrast to classic
beamforming approaches - operates on a single microphone. The system runs in
real time on a laptop, suggesting that large-scale deployment on hearing aid
chips could be achieved within a few years. Deep learning-based denoising
therefore holds the potential to improve the quality of life of millions of
hearing impaired people soon.
