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dc.contributor.authorBEAR, Hen_US
dc.contributor.authorBENETOS, Een_US
dc.contributor.authorWorkshop on Detection and Classification of Acoustic Scenes and Eventsen_US
dc.date.accessioned2018-10-05T09:12:10Z
dc.date.available2018-09-14en_US
dc.date.issued2018-11-19en_US
dc.date.submitted2018-09-26T16:11:32.766Z
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/45944
dc.description.abstractWe present a new extensible and divisible taxonomy for open set sound scene analysis. This new model allows complex scene analysis with tangible descriptors and perception labels. Its novel structure is a cluster graph such that each cluster (or subset) can stand alone for targeted analyses such as office sound event detection, whilst maintaining integrity over the whole graph (superset) of labels. The key design benefit is its extensibility as new labels are needed during new data capture. Furthermore, datasets which use the same taxonomy are easily augmented, saving future data collection effort. We balance the details needed for complex scene analysis with avoiding 'the taxonomy of everything' with our framework to ensure no duplicity in the superset of labels and demonstrate this with DCASE challenge classifications.en_US
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
dc.titleAn extensible cluster-graph taxonomy for open set sound scene analysisen_US
dc.typeConference Proceeding
dc.rights.holder© The Author(s) 2018
pubs.notesNo embargoen_US
pubs.publication-statusAccepteden_US
pubs.publisher-urlhttp://dcase.community/workshop2018/en_US
dcterms.dateAccepted2018-09-14en_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US
qmul.funderA Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineeringen_US


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