Lujun Li
Impact in
- Signal Processing top 10%
- Speech and Audio Processing
- Music and Audio Processing
- Artificial Intelligence top 10%
- Speech Recognition and Synthesis
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
Papers in
-
- Speech and Audio Processing 9
- Music and Audio Processing 8
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- Speech Recognition and Synthesis 10
- Domain Adaptation and Few-Shot Learning 4
- Topic Modeling 3
- Natural Language Processing Techniques 2
- Co-authors
- Gerhard Rigoll (10 shared papers)Ruodu Wang (1 shared paper)Jingping Yang (1 shared paper)Ya Yun Yang (1 shared paper)Yahan Yang (2 shared papers)Zhe Jin (2 shared papers)Zhengxing Sun (1 shared paper)Xin Niu (1 shared paper)
In The Last Decade
Lujun Li
20 papers receiving 226 citations
Peers
Comparison fields: 5 of 52
- Signal Processing 71
- Artificial Intelligence 116
- Computer Vision and Pattern Recognition 63
- Management Science and Operations Research 26
- Finance 11
Countries citing papers authored by Lujun Li
This map shows the geographic impact of Lujun Li'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 Lujun Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lujun Li more than expected).
Fields of papers citing papers by Lujun Li
This network shows the impact of papers produced by Lujun Li. 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 Lujun Li. The network helps show where Lujun Li may publish in the future.
Co-authors
The 13 scholars most cited alongside Lujun Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 54 | |
| 2 | 2018 | 27 | |
| 3 | 2023 | 24 | |
| 4 | 2021 | 17 | |
| 5 | 2023 | 16 | |
| 6 | 2024 | 16 | |
| 7 | 2022 | 14 | |
| 8 | 2024 | 11 | |
| 9 | 2025 | 10 | |
| 10 | 2021 | 10 | |
| 11 | 2022 | 9 | |
| 12 | 2021 | 6 | |
| 13 | 2019 | 5 | |
| 14 | 2021 | 3 | |
| 15 | 2021 | 2 | |
| 16 | 2020 | 2 | |
| 17 | Ordering strategy of short life-cycle products based on the demand forecasting | 2013 | 1 |
| 18 | 2016 | 1 | |
| 19 | 2020 | 1 | |
| 20 | 2019 | 1 |
About Lujun Li
Lujun Li is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research and Management Information Systems, having authored 21 papers that have together received 230 indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (10 papers), Speech and Audio Processing (9 papers), Music and Audio Processing (8 papers), Advanced Image and Video Retrieval Techniques (5 papers), Domain Adaptation and Few-Shot Learning (4 papers), Advanced Neural Network Applications (4 papers), Topic Modeling (3 papers) and Natural Language Processing Techniques (2 papers). The work is most often cited by research in Signal Processing (71 citations), Artificial Intelligence (116 citations), Computer Vision and Pattern Recognition (63 citations), Management Science and Operations Research (26 citations) and Finance (11 citations). Lujun Li has collaborated with scholars based in Germany, China and Hong Kong. Frequent co-authors include Gerhard Rigoll, Ruodu Wang, Jingping Yang, Ya Yun Yang, Yahan Yang, Zhe Jin, Zhengxing Sun, Xin Niu, Zhiliang Tian and Radu State. Their work appears in journals such as Applied Sciences, SIAM Journal on Financial Mathematics, IEEE Access, Electronics and Lecture notes in computer science.
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.