dc.contributor.author | BEAR, H | en_US |
dc.contributor.author | BENETOS, E | en_US |
dc.contributor.author | Workshop on Detection and Classification of Acoustic Scenes and Events | en_US |
dc.date.accessioned | 2018-10-05T09:12:10Z | |
dc.date.available | 2018-09-14 | en_US |
dc.date.issued | 2018-11-19 | en_US |
dc.date.submitted | 2018-09-26T16:11:32.766Z | |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/45944 | |
dc.description.abstract | We 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.rights | This 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.title | An extensible cluster-graph taxonomy for open set sound scene analysis | en_US |
dc.type | Conference Proceeding | |
dc.rights.holder | © The Author(s) 2018 | |
pubs.notes | No embargo | en_US |
pubs.publication-status | Accepted | en_US |
pubs.publisher-url | http://dcase.community/workshop2018/ | en_US |
dcterms.dateAccepted | 2018-09-14 | en_US |
qmul.funder | A Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineering | en_US |
qmul.funder | A Machine Learning Framework for Audio Analysis and Retrieval::Royal Academy of Engineering | en_US |