Detection and Classification of Acoustic Scenes and Events
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 419
- Type
- D - Journal article
- DOI
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10.1109/TMM.2015.2428998
- Title of journal
- IEEE Transactions on Multimedia
- Article number
- 10
- First page
- 1733
- Volume
- 17
- Issue
- 10
- ISSN
- 1520-9210
- Open access status
- Out of scope for open access requirements
- Month of publication
- October
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 201
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper founded the specialist field now studied in an annual "DCASE workshop" (http://dcase.community). It specified a set of tasks for detecting/classifying in everyday sound scenes, and reported the outcomes of an international data-driven challenge addressing them. This paper is the #1 most-downloaded paper in this premier IEEE journal, in 2016 and in 2017. Thanks to this paper and associated work, "DCASE" is now a regular topic in the IEEE's flagship conference ICASSP (49% acceptance, 3100 delegates, https://2019.ieeeicassp.org/). DCASE influenced industry work e.g. Google's AudioSet https://research.google.com/audioset/
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -