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Active Learning for Sound Event Classification by Clustering Unlabeled Data

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE
Pages751-755
ISBN (Electronic)978-1-5090-4117-6
DOIs
Publication statusPublished - 2017
Publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech and Signal Processing -
Duration: 1 Jan 19001 Jan 2000

Publication series

Name
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Period1/01/001/01/00

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.

Keywords

  • active learning, sound event classification, K-medoids clustering

Publication forum classification

Field of science, Statistics Finland