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:
this https URL We believe this result
could help real-world applications in selective hearing, noise cancellation,
and audio augmented reality.

本文提出一种两阶段的自我监督框架，旨在实现耳机和助听器等双耳应用中的音频语音分离，该方法通过区域方式将具有细粒度空间属性的自适应信号从底噪中分离出来进行训练，并表现出远超通用监督学习的性能。