Automatic transcription of Turkish microtonal music
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 420
- Type
- D - Journal article
- DOI
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10.1121/1.4930187
- Title of journal
- Journal of the Acoustical Society of America
- Article number
- -
- First page
- 2118
- Volume
- 138
- Issue
- 4
- ISSN
- 1520-8524
- 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
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1
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Multi-pitch detection model was implemented as the "Silvet" plugin (https://code.soundsoftware.ac.uk/projects/silvet), with over 26,000 downloads. Model ranked 1st at the MIREX 2015 contest for Multiple-F0 Estimation (https://www.music-ir.org/mirex/wiki/2015:Multiple_Fundamental_Frequency_Estimation_%26_Tracking_Results_-_MIREX_Dataset). Work led to keynote talks (8th FMA workshop 2018; 1st TROMPA Workshop 2019), a tutorial (NUS, Singapore, 2019), and invited talks (172nd ASA meeting, 2016; ISCEA symposium, 2017; 5th AAWM conference, 2018; Digital Musicology and Libraries workshop, 2019). Work has led to 2 funded PhD projects in automatic music transcription as part of the H2020-funded "MIP-Frontiers" European Training Network (grant no. 765068) and the UKRI CDT in AI+Music (EP/S022694/1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -