Kun Han
Impact in
- Signal Processing top 0.5%
- Speech and Audio Processing
- Music and Audio Processing
- Blind Source Separation Techniques
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- Emotion and Mood Recognition
Papers in
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- Speech and Audio Processing 12
- Music and Audio Processing 6
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- Advanced Image and Video Retrieval Techniques 5
- Journals
- IEEE/ACM Transactions on Audio Speech and Language Processing (2 papers)IEEE Transactions on Audio Speech and Language Processing (2 papers)Information Sciences (1 paper)IEEE Geoscience and Remote Sensing Letters (1 paper)The Journal of the Acoustical Society of America (1 paper)
- Partner nations
- ChinaUnited StatesCanada
In The Last Decade
Kun Han
28 papers receiving 1.3k citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Signal Processing 999
- Experimental and Cognitive Psychology 471
- Artificial Intelligence 708
- Cognitive Neuroscience 212
- Computer Vision and Pattern Recognition 215
Countries citing papers authored by Kun Han
This map shows the geographic impact of Kun Han'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 Kun Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kun Han more than expected).
Fields of papers citing papers by Kun Han
This network shows the impact of papers produced by Kun Han. 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 Kun Han. The network helps show where Kun Han may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Kun Han, 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 | 2025 | 1 | |
| 2 | 2024 | 17 | |
| 3 | 2021 | 1 | |
| 4 | 2021 | 5 | |
| 5 | 2021 | 22 | |
| 6 | 2020 | 1 | |
| 7 | 2019 | 119 | |
| 8 | 2015 | 36 | |
| 9 | 2015 | 191 | |
| 10 | 2014 | 3 | |
| 11 | 2014 | 0 | |
| 12 | Speech emotion recognition using deep neural network and extreme learning machine Hit paper breakdown → | 2014 | 524 |
| 13 | 2014 | 67 | |
| 14 | 2012 | 22 | |
| 15 | 2011 | 25 | |
| 16 | 2011 | 1 | |
| 17 | 2010 | 1 | |
| 18 | 2009 | 4 | |
| 19 | 2009 | 1 | |
| 20 | THE DISTURBANCE-RESISTIBILITY OF LINEAR CONTROL SYSTEMS | 1981 | 1 |
About Kun Han
Kun Han is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Media Technology and Computational Mechanics, having authored 35 papers that have together received 1.4k indexed citations. Recurring topics across this work include Speech and Audio Processing (12 papers), Speech Recognition and Synthesis (9 papers), Music and Audio Processing (6 papers), Advanced Image and Video Retrieval Techniques (5 papers), Advanced Adaptive Filtering Techniques (4 papers), Hearing Loss and Rehabilitation (4 papers), Topic Modeling (3 papers) and Remote-Sensing Image Classification (3 papers). The work is most often cited by research in Signal Processing (999 citations), Experimental and Cognitive Psychology (471 citations), Artificial Intelligence (708 citations), Cognitive Neuroscience (212 citations) and Computer Vision and Pattern Recognition (215 citations). Kun Han has collaborated with scholars based in China, United States and Canada. Frequent co-authors include DeLiang Wang, Dong Yu, Ivan Tashev, Yuxuan Wang, Yuxuan Wang, William S. Woods, Tao Zhang, Ivo Merks, Xiangang Li and Yun Wang. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Transactions on Audio Speech and Language Processing, Information Sciences, IEEE Geoscience and Remote Sensing Letters and The Journal of the Acoustical Society of America.
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.