Active Learning for Sound Event Classification by Clustering Unlabeled Data

Abstract

This paper proposes a novel active learning method to save annotation effort when preparing material to train sound event classifiers. K-medoids clustering is performed on unlabeled sound segments, and medoids of clusters are presented to annotators for labeling. The annotated label for a medoid is used to derive predicted labels for other cluster members. The obtained labels are used to build a classifier using supervised training. The accuracy of the resulted classifier is used to evaluate the performance of the proposed method. The evaluation made on a public environmental sound dataset shows that the proposed method outperforms reference methods (random sampling, certainty-based active learning and semi-supervised learning) with all simulated labeling budgets, the number of available labeling responses. Through all the experiments, the proposed method saves 50%–60% labeling budget to achieve the same accuracy, with respect to the best reference method.