Xiaoyu Bie
Impact in
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 10%
- Speech Recognition and Synthesis
- Anomaly Detection Techniques and Applications
Papers in
-
- Speech Recognition and Synthesis 4
- Computational Physics and Python Applications 1
- Anomaly Detection Techniques and Applications 1
-
- Music and Audio Processing 5
- Speech and Audio Processing 3
- Co-authors
- Xavier Alameda-Pineda (6 shared papers)Laurent Girin (5 shared papers)Simon Leglaive (5 shared papers)Thomas Hueber (3 shared papers)Julien Diard (2 shared papers)W. Guo (1 shared paper)Francesc Moreno-Noguer (1 shared paper)Xiaoyu Lin (1 shared paper)
- Journals
- IEEE/ACM Transactions on Audio Speech and Language Processing (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)arXiv (Cornell University) (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (2 papers)
In The Last Decade
Xiaoyu Bie
6 papers receiving 210 citations
Peers
Comparison fields: 5 of 75
- Signal Processing 68
- Artificial Intelligence 102
- Computer Vision and Pattern Recognition 61
- Statistical and Nonlinear Physics 19
- Health Informatics 2
Countries citing papers authored by Xiaoyu Bie
This map shows the geographic impact of Xiaoyu Bie'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 Xiaoyu Bie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoyu Bie more than expected).
Fields of papers citing papers by Xiaoyu Bie
This network shows the impact of papers produced by Xiaoyu Bie. 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 Xiaoyu Bie. The network helps show where Xiaoyu Bie may publish in the future.
Co-authors
The 8 scholars most cited alongside Xiaoyu Bie, 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 | 2021 | 96 | |
| 2 | 2022 | 42 | |
| 3 | 2021 | 41 | |
| 4 | 2022 | 38 | |
| 5 | 2021 | 4 | |
| 6 | 2023 | 1 |
About Xiaoyu Bie
Xiaoyu Bie is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Control and Systems Engineering and Infectious Diseases, having authored 6 papers that have together received 222 indexed citations. Recurring topics across this work include Music and Audio Processing (5 papers), Speech Recognition and Synthesis (4 papers), Speech and Audio Processing (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Computational Physics and Python Applications (1 paper), Human Motion and Animation (1 paper), Human Pose and Action Recognition (1 paper) and Anomaly Detection Techniques and Applications (1 paper). The work is most often cited by research in Signal Processing (68 citations), Artificial Intelligence (102 citations), Computer Vision and Pattern Recognition (61 citations), Statistical and Nonlinear Physics (19 citations) and Health Informatics (2 citations). Xiaoyu Bie has collaborated with scholars based in France, Spain and Belgium. Frequent co-authors include Xavier Alameda-Pineda, Laurent Girin, Simon Leglaive, Thomas Hueber, Julien Diard, W. Guo, Francesc Moreno-Noguer and Xiaoyu Lin. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), arXiv (Cornell University) and HAL (Le Centre pour la Communication Scientifique Directe).
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.