Binaural Angular Separation Network
Yang Yang George Sung Shao-Fu Shih Hakan Erdogan Chehung Lee Matthias Grundmann
We propose a neural network model that can separate target speech sources
from interfering sources at different angular regions using two microphones.
The model is trained with simulated room impulse responses (RIRs) using
omni-directional microphones without needing to collect real RIRs. By relying
on specific angular regions and multiple room simulations, the model utilizes
consistent time difference of arrival (TDOA) cues, or what we call delay
contrast, to separate target and interference sources while remaining robust in
various reverberation environments. We demonstrate the model is not only
generalizable to a commercially available device with a slightly different
microphone geometry, but also outperforms our previous work which uses one
additional microphone on the same device. The model runs in real-time on-device
and is suitable for low-latency streaming applications such as telephony and
video conferencing.