  Active speaker detection (ASD) and virtual cinematography (VC) can
significantly improve the remote user experience of a video conference by
automatically panning, tilting and zooming of a video conferencing camera:
users subjectively rate an expert video cinematographer's video significantly
higher than unedited video. We describe a new automated ASD and VC that
performs within 0.3 MOS of an expert cinematographer based on subjective
ratings with a 1-5 scale. This system uses a 4K wide-FOV camera, a depth
camera, and a microphone array; it extracts features from each modality and
trains an ASD using an AdaBoost machine learning system that is very efficient
and runs in real-time. A VC is similarly trained using machine learning to
optimize the subjective quality of the overall experience. To avoid distracting
the room participants and reduce switching latency the system has no moving
parts -- the VC works by cropping and zooming the 4K wide-FOV video stream. The
system was tuned and evaluated using extensive crowdsourcing techniques and
evaluated on a dataset with N=100 meetings, each 2-5 minutes in length.
