  This paper proposes PickNet, a neural network model for real-time channel
selection for an ad hoc microphone array consisting of multiple recording
devices like cell phones. Assuming at most one person to be vocally active at
each time point, PickNet identifies the device that is spatially closest to the
active person for each time frame by using a short spectral patch of just
hundreds of milliseconds. The model is applied to every time frame, and the
short time frame signals from the selected microphones are concatenated across
the frames to produce an output signal. As the personal devices are usually
held close to their owners, the output signal is expected to have higher
signal-to-noise and direct-to-reverberation ratios on average than the input
signals. Since PickNet utilizes only limited acoustic context at each time
frame, the system using the proposed model works in real time and is robust to
changes in acoustic conditions. Speech recognition-based evaluation was carried
out by using real conversational recordings obtained with various smartphones.
The proposed model yielded significant gains in word error rate with limited
computational cost over systems using a block-online beamformer and a single
distant microphone.
