  The largest source of sound events is web videos. Most videos lack sound
event labels at segment level, however, a significant number of them do respond
to text queries, from a match found using metadata by search engines. In this
paper we explore the extent to which a search query can be used as the true
label for detection of sound events in videos. We present a framework for
large-scale sound event recognition on web videos. The framework crawls videos
using search queries corresponding to 78 sound event labels drawn from three
datasets. The datasets are used to train three classifiers, and we obtain a
prediction on 3.7 million web video segments. We evaluated performance using
the search query as true label and compare it with human labeling. Both types
of ground truth exhibited close performance, to within 10%, and similar
performance trend with increasing number of evaluated segments. Hence, our
experiments show potential for using search query as a preliminary true label
for sound event recognition in web videos.
