dc.contributor.author | Bear, H | |
dc.contributor.author | Morfi, G | |
dc.contributor.author | Benetos, E | |
dc.contributor.author | 22nd Annual Conference of the International Speech Communication Association (INTERSPEECH) | |
dc.date.accessioned | 2021-07-01T11:01:37Z | |
dc.date.available | 2021-06-02 | |
dc.date.available | 2021-07-01T11:01:37Z | |
dc.date.issued | 2021-08-30 | |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/72840 | |
dc.description.abstract | Sound scene geotagging is a new topic of research which has evolved from acoustic scene classification. It is motivated by the idea of audio surveillance. Not content with only describing a scene in a recording, a machine which can locate where the recording was captured would be of use to many. In this paper we explore a series of common audio data augmentation methods to evaluate which best improves the accuracy of audio geotagging classifiers. Our work improves on the state-of-the-art city geotagging method by 23% in terms of classification accuracy. | en_US |
dc.format.extent | ? - ? (5) | |
dc.publisher | International Speech and Communication Association (ISCA) | en_US |
dc.title | An evaluation of data augmentation methods for sound scene geotagging | en_US |
dc.type | Conference Proceeding | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
pubs.publisher-url | https://www.interspeech2021.org/ | en_US |
dcterms.dateAccepted | 2021-06-02 | |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |