Jian Wu
- Artificial Intelligence top 0.5%
- Signal Processing top 0.2%
- Computer Vision and Pattern Recognition top 2%
- Information Systems top 2%
- Computational Theory and Mathematics top 2%
- Topics
- Speech Recognition and Synthesis (56 papers)Speech and Audio Processing (54 papers)Music and Audio Processing (41 papers)
- Journals
- International Journal of Computer VisionArtificial IntelligenceIEEE Transactions on Cybernetics
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Jian Wu
81 papers receiving 3.4k citations
Hit Papers
Peers
Comparison fields: 5 of 155
- Artificial Intelligence 2.2k
- Signal Processing 2.0k
- Computer Vision and Pattern Recognition 467
- Information Systems 339
- Computational Theory and Mathematics 277
Countries citing papers authored by Jian Wu
This map shows the geographic impact of Jian Wu'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 Jian Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jian Wu more than expected).
Fields of papers citing papers by Jian Wu
This network shows the impact of papers produced by Jian Wu. 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 Jian Wu. The network helps show where Jian Wu may publish in the future.
Co-authorship network of co-authors of Jian Wu
This figure shows the co-authorship network connecting the top 25 collaborators of Jian Wu. A scholar is included among the top collaborators of Jian Wu 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 Jian Wu. Jian Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 10 | |
| 5 | 39 | |
| 6 | 20 | |
| 7 | 2 | |
| 8 | 103 | |
| 9 | Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based modelsbreakdown → | 388 |
| 10 | 9 | |
| 11 | DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancementbreakdown → | 432 |
| 12 | 18 | |
| 13 | 13 | |
| 14 | 21 | |
| 15 | 3 | |
| 16 | 86 | |
| 17 | Robust speech recognition using cepstral minimum-mean-square-error noise suppressor | 5 |
| 18 | 60 | |
| 19 | 21 | |
| 20 | 26 |
About Jian Wu
Jian Wu is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 84 papers that have together received 3.6k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (56 papers), Speech and Audio Processing (54 papers) and Music and Audio Processing (41 papers). The work is most often cited by research in Signal Processing (2.0k citations), Artificial Intelligence (2.2k citations) and Computer Vision and Pattern Recognition (467 citations). Jian Wu has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Jinyu Li, Takuya Yoshioka, Xiong Xiao, Shujie Liu, Lei Xie, Yu Wu, Sanyuan Chen, Tianyan Zhou, Chengyi Wang and Naoyuki Kanda. Their work appears in journals such as International Journal of Computer Vision, Artificial Intelligence and IEEE Transactions on Cybernetics.
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.