A morphological model for simulating acoustic scenes and its application to sound event detection
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 450
- Type
- D - Journal article
- DOI
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10.1109/TASLP.2016.2587218
- Title of journal
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Article number
- -
- First page
- 1854
- Volume
- 24
- Issue
- 10
- ISSN
- 2329-9290
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2016
- 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
- 14
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper's main contribution on generating soundscapes for the development of sound recognition systems was key towards securing EPSRC Grant EP/R01891X/1 (2018-2019) and a Turing Fellowship at The Alan Turing Institute (EP/N510129/1, 2018-2020) in the topic of urban sound monitoring. Work led to collaboration with Cirrus Logic Ltd on audio context recognition (contact: Toby Stokes, Toby.Stokes@cirrus.com), and to several invited talks (Audio Analytic Ltd, UK; NUS, Singapore; Intelligent Sensing Summer School, UK). Proposed system was used to generate data for the 2016 IEEE DCASE Challenge (http://www.cs.tut.fi/sgn/arg/dcase2016/), attracting over 80 submissions from international teams.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -