Cyril Joder
Impact in
- Signal Processing top 5%
- Music and Audio Processing
- Speech and Audio Processing
- Blind Source Separation Techniques
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- Music Technology and Sound Studies
Papers in
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- Speech and Audio Processing 9
- Music and Audio Processing 9
- Blind Source Separation Techniques 1
- Time Series Analysis and Forecasting 1
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- Music Technology and Sound Studies 5
- Co-authors
- Slim Essid (4 shared papers)Gaël Richard (3 shared papers)Björn W. Schuller (6 shared papers)Gaël Richard (1 shared paper)Felix Weninger (2 shared papers)Martin Wöllmer (1 shared paper)
- Journals
- IEEE Transactions on Audio Speech and Language Processing (3 papers)SPIRE - Sciences Po Institutional REpository (1 paper)mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (2 papers)OPUS (Augsburg University) (3 papers)
In The Last Decade
Cyril Joder
10 papers receiving 174 citations
Peers
Comparison fields: 5 of 24
- Signal Processing 172
- Computer Vision and Pattern Recognition 104
- Music 10
- Developmental Biology 6
- Artificial Intelligence 39
Countries citing papers authored by Cyril Joder
This map shows the geographic impact of Cyril Joder'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 Cyril Joder with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Cyril Joder more than expected).
Fields of papers citing papers by Cyril Joder
This network shows the impact of papers produced by Cyril Joder. 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 Cyril Joder. The network helps show where Cyril Joder may publish in the future.
Co-authors
The 6 scholars most cited alongside Cyril Joder, 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 | 2009 | 74 | |
| 2 | 2011 | 39 | |
| 3 | 2013 | 14 | |
| 4 | Exploring Nonnegative Matrix Factorization for Audio Classification: Application to Speaker Recognition | 2012 | 13 |
| 5 | 2013 | 10 | |
| 6 | 2012 | 9 | |
| 7 | The TUM Cumulative DTW Approach for the Mediaeval 2012 Spoken Web Search Task | 2012 | 7 |
| 8 | 2013 | 6 | |
| 9 | 2013 | 6 | |
| 10 | 2010 | 5 |
About Cyril Joder
Cyril Joder is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics and Infectious Diseases, having authored 10 papers that have together received 183 indexed citations. Recurring topics across this work include Speech and Audio Processing (9 papers), Music and Audio Processing (9 papers), Music Technology and Sound Studies (5 papers), Advanced Text Analysis Techniques (1 paper), Blind Source Separation Techniques (1 paper), Time Series Analysis and Forecasting (1 paper), Speech Recognition and Synthesis (1 paper) and Advanced Adaptive Filtering Techniques (1 paper). The work is most often cited by research in Signal Processing (172 citations), Computer Vision and Pattern Recognition (104 citations), Music (10 citations), Developmental Biology (6 citations) and Artificial Intelligence (39 citations). Cyril Joder has collaborated with scholars based in France and Germany. Frequent co-authors include Slim Essid, Gaël Richard, Björn W. Schuller, Gaël Richard, Felix Weninger and Martin Wöllmer. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, SPIRE - Sciences Po Institutional REpository, mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) and OPUS (Augsburg University).
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.