Show simple item record

dc.contributor.authorWei, Wen_US
dc.contributor.authorZhu, Hen_US
dc.contributor.authorBenetos, Een_US
dc.contributor.authorWang, Yen_US
dc.contributor.authorIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020)en_US
dc.date.accessioned2020-04-08T08:34:23Z
dc.date.available2020-01-24en_US
dc.date.issued2020-05-04en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/63518
dc.description.abstractThis 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.en_US
dc.format.extent? - ? (5)en_US
dc.publisherIEEEen_US
dc.titleA-CRNN: a domain adaptation model for sound event detectionen_US
dc.typeConference Proceeding
dc.rights.holder© 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.
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
pubs.publisher-urlhttps://2020.ieeeicassp.org/en_US
dcterms.dateAccepted2020-01-24en_US
rioxxterms.funderDefault funderen_US
rioxxterms.identifier.projectDefault projecten_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US
rioxxterms.funder.project483cf8e1-88a1-4b8b-aecb-8402672d45f8en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record