Unsupervised Adversarial Domain Adaptation for Acoustic Scene Classification

Shayan Gharib, Konstantinos Drossos, Emre Cakir, Dmitriy Serdyuk, Tuomas Virtanen

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


    A general problem in acoustic scene classification task is the mismatched conditions between training and testing data, which significantly reduces the performance of the developed methods on classification accuracy. As a countermeasure, we present the first method of unsupervised adversarial domain adaptation for acoustic scene classification. We employ a model pre-trained on data from one set of conditions and by using data from other set of conditions, we adapt the model in order that its output cannot be used for classifying the set of conditions that input data belong to. We use a freely available dataset from the DCASE 2018 challenge Task 1, subtask B, that contains data from mismatched recording devices. We consider the scenario where the annotations are available for the data recorded from one device, but not for the rest. Our results show that with our model agnostic method we can achieve ∼10% increase at the accuracy on an unseen and unlabeled dataset, while keeping almost the same performance on the labeled dataset.
    Original languageEnglish
    Title of host publicationProceedings of the Detection and Classification of Acoustic Scenes and Events 2018 Workshop (DCASE2018)
    PublisherTampere University of Technology
    ISBN (Electronic)978-952-15-4262-6
    Publication statusPublished - 2018
    Publication typeA4 Article in a conference publication
    EventDetection and Classification of Acoustic Scenes and Events -
    Duration: 19 Nov 201820 Nov 2018


    ConferenceDetection and Classification of Acoustic Scenes and Events

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