Active Learning for Sound Event Classification by Clustering Unlabeled Data

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

    14 Citations (Scopus)
    79 Downloads (Pure)

    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.
    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

    Keywords

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

    Publication forum classification

    • Publication forum level 1

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