F. Desobry
Impact in
- Signal Processing top 10%
- Time Series Analysis and Forecasting
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 10%
- Anomaly Detection Techniques and Applications
- Data Stream Mining Techniques
Papers in
-
- Blind Source Separation Techniques 4
- Music and Audio Processing 2
- Speech and Audio Processing 2
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- Anomaly Detection Techniques and Applications 2
- Co-authors
- Manuel Davy (7 shared papers)C. Doncarli (2 shared papers)Arthur Gretton (2 shared papers)Elizabeth Garnsey (2 shared papers)William J. Fitzgerald (2 shared papers)Cédric Févotte (1 shared paper)Stéphane Canu (1 shared paper)
- Journals
- IEEE Transactions on Signal Processing (1 paper)Signal Processing (1 paper)Energy Policy (1 paper)Cambridge University Engineering Department Publications Database (1 paper)Cambridge University Press eBooks (1 paper)
- Partner nations
- FranceUnited KingdomGermany
In The Last Decade
F. Desobry
11 papers receiving 349 citations
Peers
Comparison fields: 5 of 68
- Signal Processing 100
- Artificial Intelligence 196
- Statistics, Probability and Uncertainty 38
- Statistics and Probability 33
- Control and Systems Engineering 79
Countries citing papers authored by F. Desobry
This map shows the geographic impact of F. Desobry's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by F. Desobry with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites F. Desobry more than expected).
Fields of papers citing papers by F. Desobry
This network shows the impact of papers produced by F. Desobry. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by F. Desobry. The network helps show where F. Desobry may publish in the future.
Co-authors
The 7 scholars most cited alongside F. Desobry, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2005 | 190 | |
| 2 | 2005 | 100 | |
| 3 | 2010 | 28 | |
| 4 | 2003 | 23 | |
| 5 | 2004 | 15 | |
| 6 | A Class of Kernels for Sets of Vectors | 2005 | 8 |
| 7 | 2006 | 6 | |
| 8 | 2004 | 5 | |
| 9 | 2006 | 5 | |
| 10 | 2007 | 4 | |
| 11 | 2011 | 1 |
About F. Desobry
F. Desobry is a scholar working on Signal Processing, Artificial Intelligence, Control and Systems Engineering, Building and Construction and Electrical and Electronic Engineering, having authored 11 papers that have together received 385 indexed citations. Recurring topics across this work include Blind Source Separation Techniques (4 papers), Music and Audio Processing (2 papers), Energy Load and Power Forecasting (2 papers), Anomaly Detection Techniques and Applications (2 papers), Building Energy and Comfort Optimization (2 papers), Fault Detection and Control Systems (2 papers), Statistical Methods and Inference (2 papers) and Speech and Audio Processing (2 papers). The work is most often cited by research in Signal Processing (100 citations), Artificial Intelligence (196 citations), Statistics, Probability and Uncertainty (38 citations), Statistics and Probability (33 citations) and Control and Systems Engineering (79 citations). F. Desobry has collaborated with scholars based in France, United Kingdom and Germany. Frequent co-authors include Manuel Davy, C. Doncarli, Arthur Gretton, Elizabeth Garnsey, William J. Fitzgerald, Cédric Févotte and Stéphane Canu. Their work appears in journals such as IEEE Transactions on Signal Processing, Signal Processing, Energy Policy, Cambridge University Engineering Department Publications Database and Cambridge University Press eBooks.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.