  In crowded settings, the human brain can focus on speech from a target
speaker, given prior knowledge of how they sound. We introduce a novel
intelligent hearable system that achieves this capability, enabling target
speech hearing to ignore all interfering speech and noise, but the target
speaker. A naive approach is to require a clean speech example to enroll the
target speaker. This is however not well aligned with the hearable application
domain since obtaining a clean example is challenging in real world scenarios,
creating a unique user interface problem. We present the first enrollment
interface where the wearer looks at the target speaker for a few seconds to
capture a single, short, highly noisy, binaural example of the target speaker.
This noisy example is used for enrollment and subsequent speech extraction in
the presence of interfering speakers and noise. Our system achieves a signal
quality improvement of 7.01 dB using less than 5 seconds of noisy enrollment
audio and can process 8 ms of audio chunks in 6.24 ms on an embedded CPU. Our
user studies demonstrate generalization to real-world static and mobile
speakers in previously unseen indoor and outdoor multipath environments.
Finally, our enrollment interface for noisy examples does not cause performance
degradation compared to clean examples, while being convenient and
user-friendly. Taking a step back, this paper takes an important step towards
enhancing the human auditory perception with artificial intelligence. We
provide code and data at: https://github.com/vb000/LookOnceToHear.
