Audio Fingerprinting for Multi-Device Self-Localisation
- Submitting institution
-
Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 410
- Type
- D - Journal article
- DOI
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10.1109/TASLP.2015.2442417
- Title of journal
- IEEE Transactions on Audio, Speech and Language Processing
- Article number
- -
- First page
- 1623
- Volume
- 23
- Issue
- 10
- ISSN
- 1558-7916
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- 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
-
3
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This is the first of four IEEE Transactions publications from EPSRC project EP/K007491/1, £363K, 2013-16. It allows one to localise an arbitrary number of randomly distributed devices in a noisy environment. It advances the field far beyond competing techniques that all make unrealistic assumptions about the signals and sensors, or perform very limited localisation. With relevance to navigation, robotics and audio processing, this work leads to new robust applications across many disciplines.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -