  We consider the problem of audio voice separation for binaural applications,
such as earphones and hearing aids. While today's neural networks perform
remarkably well (separating $4+$ sources with 2 microphones) they assume a
known or fixed maximum number of sources, K. Moreover, today's models are
trained in a supervised manner, using training data synthesized from generic
sources, environments, and human head shapes.
  This paper intends to relax both these constraints at the expense of a slight
alteration in the problem definition. We observe that, when a received mixture
contains too many sources, it is still helpful to separate them by region,
i.e., isolating signal mixtures from each conical sector around the user's
head. This requires learning the fine-grained spatial properties of each
region, including the signal distortions imposed by a person's head. We propose
a two-stage self-supervised framework in which overheard voices from earphones
are pre-processed to extract relatively clean personalized signals, which are
then used to train a region-wise separation model. Results show promising
performance, underscoring the importance of personalization over a generic
supervised approach. (audio samples available at our project website:
https://uiuc-earable-computing.github.io/binaural/. We believe this result
could help real-world applications in selective hearing, noise cancellation,
and audio augmented reality.
