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    A-CRNN: a domain adaptation model for sound event detection 
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    A-CRNN: a domain adaptation model for sound event detection

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    Accepted version (324.9Kb)
    Pagination
    ? - ? (5)
    Publisher
    IEEE
    Publisher URL
    https://2020.ieeeicassp.org/
    Metadata
    Show full item record
    Abstract
    This paper presents a domain adaptation model for sound event detection. A common challenge for sound event detection is how to deal with the mismatch among different datasets. Typically, the performance of a model will decrease if it is tested on a dataset which is different from the one that the model is trained on. To address this problem, based on convolutional recurrent neural networks (CRNNs), we propose an adapted CRNN (A-CRNN) as an unsupervised adversarial domain adaptation model for sound event detection. We have collected and annotated a dataset in Singapore with two types of recording devices to complement existing datasets in the research community, especially with respect to domain adaptation. We perform experiments on recordings from different datasets and from different recordings devices. Our experimental results show that the proposed A-CRNN model can achieve a better performance on an unseen dataset in comparison with the baseline non-adapted CRNN model.
    Authors
    Wei, W; Zhu, H; Benetos, E; Wang, Y; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)
    URI
    https://qmro.qmul.ac.uk/xmlui/handle/123456789/63518
    Collections
    • Electronic Engineering and Computer Science [2319]
    Copyright statements
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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