  Audio Event Detection (AED) aims to recognize sounds within audio and video
recordings. AED employs machine learning algorithms commonly trained and tested
on annotated datasets. However, available datasets are limited in number of
samples and hence it is difficult to model acoustic diversity. Therefore, we
propose combining labeled audio from a dataset and unlabeled audio from the web
to improve the sound models. The audio event detectors are trained on the
labeled audio and ran on the unlabeled audio downloaded from YouTube. Whenever
the detectors recognized any of the known sounds with high confidence, the
unlabeled audio was use to re-train the detectors. The performance of the
re-trained detectors is compared to the one from the original detectors using
the annotated test set. Results showed an improvement of the AED, and uncovered
challenges of using web audio from videos.

Sound;Machine Learning;Multimedia