  We present a new method and a large-scale database to detect audio-video
synchronization(A/V sync) errors in tennis videos. A deep network is trained to
detect the visual signature of the tennis ball being hit by the racquet in the
video stream. Another deep network is trained to detect the auditory signature
of the same event in the audio stream. During evaluation, the audio stream is
searched by the audio network for the audio event of the ball being hit. If the
event is found in audio, the neighboring interval in video is searched for the
corresponding visual signature. If the event is not found in the video stream
but is found in the audio stream, A/V sync error is flagged. We developed a
large-scaled database of 504,300 frames from 6 hours of videos of tennis
events, simulated A/V sync errors, and found our method achieves high accuracy
on the task.
