  On-device directional hearing requires audio source separation from a given
direction while achieving stringent human-imperceptible latency requirements.
While neural nets can achieve significantly better performance than traditional
beamformers, all existing models fall short of supporting low-latency causal
inference on computationally-constrained wearables. We present DeepBeam, a
hybrid model that combines traditional beamformers with a custom lightweight
neural net. The former reduces the computational burden of the latter and also
improves its generalizability, while the latter is designed to further reduce
the memory and computational overhead to enable real-time and low-latency
operations. Our evaluation shows comparable performance to state-of-the-art
causal inference models on synthetic data while achieving a 5x reduction of
model size, 4x reduction of computation per second, 5x reduction in processing
time and generalizing better to real hardware data. Further, our real-time
hybrid model runs in 8 ms on mobile CPUs designed for low-power wearable
devices and achieves an end-to-end latency of 17.5 ms.
