SEMI-SUPERVISED LEARNING HELPS IN SOUND EVENT CLASSIFICATION

We investigate the suitability of semi-supervised learning in sound event classification on a large database of 17 k sound clips. Seven categories are chosen based on the findsounds.com schema: animals, people, nature, vehicles, noisemakers, office, and musical instruments. Our results show that adding unlabelled sound event data to the training set based on sufficient classifier confidence level after its automatic labelling level can significantly enhance classification performance. Furthermore, combined with optimal re-sampling of originally labelled instances and iteratively learning in semi-supervised manner, the expected gain can reach approximately half the one achieved by using the originally manually labelled data. Overall, maximum performance of 71.7% can be reported for the automatic classification of sound in a large-scale archive.