Alain de Cheveigné
- Signal Processing top 0.1%
- Cognitive Neuroscience top 0.5%
- Artificial Intelligence top 0.5%
- Experimental and Cognitive Psychology top 1%
- Computer Vision and Pattern Recognition top 1%
- Co-authors
- Hideki KawaharaIkuyo Masuda-KatsuseJonathan Z. SimonDorothée ArzounianLucas C. ParraIsrael NelkenStephen McAdamsDaniel D.E. Wong
- Topics
- Hearing Loss and Rehabilitation (37 papers)Neuroscience and Music Perception (26 papers)Neural dynamics and brain function (25 papers)
- Partner nations
- FranceJapanUnited Kingdom
In The Last Decade
Alain de Cheveigné
79 papers receiving 4.9k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- Signal Processing 3.2k
- Cognitive Neuroscience 2.5k
- Artificial Intelligence 1.7k
- Experimental and Cognitive Psychology 930
- Computer Vision and Pattern Recognition 704
Countries citing papers authored by Alain de Cheveigné
This map shows the geographic impact of Alain de Cheveigné'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 Alain de Cheveigné with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alain de Cheveigné more than expected).
Fields of papers citing papers by Alain de Cheveigné
This network shows the impact of papers produced by Alain de Cheveigné. 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 Alain de Cheveigné. The network helps show where Alain de Cheveigné may publish in the future.
Co-authorship network of co-authors of Alain de Cheveigné
This figure shows the co-authorship network connecting the top 25 collaborators of Alain de Cheveigné. A scholar is included among the top collaborators of Alain de Cheveigné based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alain de Cheveigné. Alain de Cheveigné is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 3 | |
| 3 | 1 | |
| 4 | 12 | |
| 5 | 20 | |
| 6 | 29 | |
| 7 | 62 | |
| 8 | 3 | |
| 9 | 154 | |
| 10 | 59 | |
| 11 | 87 | |
| 12 | 127 | |
| 13 | Stimulus-specific adaptation measured in the guinea pig using magnetoencephalography | 1 |
| 14 | 47 | |
| 15 | Adaptive Template Matching with Shift-Invariant Semi-NMF | 9 |
| 16 | Computational auditory induction by missing-data non-negative matrix factorization. | 8 |
| 17 | 60 | |
| 18 | 167 | |
| 19 | Comparative evaluation of F0 estimation algorithms | 0 |
| 20 | 54 |
About Alain de Cheveigné
Alain de Cheveigné is a scholar working on Signal Processing, Cognitive Neuroscience and Sensory Systems, having authored 82 papers that have together received 5.3k indexed citations. Recurring topics across this work include Hearing Loss and Rehabilitation (37 papers), Neuroscience and Music Perception (26 papers) and Neural dynamics and brain function (25 papers). The work is most often cited by research in Signal Processing (3.2k citations), Cognitive Neuroscience (2.5k citations) and Experimental and Cognitive Psychology (930 citations). Alain de Cheveigné has collaborated with scholars based in France, Japan and United Kingdom. Frequent co-authors include Hideki Kawahara, Ikuyo Masuda-Katsuse, Jonathan Z. Simon, Dorothée Arzounian, Lucas C. Parra, Israel Nelken, Stephen McAdams, Daniel D.E. Wong, Giovanni M. Di Liberto and Jens Hjortkjær. Their work appears in journals such as Neuron, Journal of Neuroscience and NeuroImage.
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.