Yang Ai
Impact in
- Signal Processing top 5%
- Speech and Audio Processing
- Music and Audio Processing
- Blind Source Separation Techniques
- Artificial Intelligence top 10%
- Speech Recognition and Synthesis
- Neural Networks and Applications
Papers in
-
- Speech and Audio Processing 32
- Music and Audio Processing 15
- Blind Source Separation Techniques 4
-
- Speech Recognition and Synthesis 30
- Neural Networks and Applications 5
- Natural Language Processing Techniques 4
- Co-authors
- Zhen-Hua Ling (32 shared papers)Li-Rong Dai (2 shared papers)Yu Gu (1 shared paper)Zhanshan Li (3 shared papers)Junichi Yamagishi (3 shared papers)Xin Wang (2 shared papers)Kun Wang (1 shared paper)Changhai Zhang (1 shared paper)
In The Last Decade
Yang Ai
30 papers receiving 199 citations
Peers
Comparison fields: 5 of 26
- Signal Processing 166
- Artificial Intelligence 136
- Computer Vision and Pattern Recognition 52
- Pharmacy 6
- Computational Mechanics 15
Countries citing papers authored by Yang Ai
This map shows the geographic impact of Yang Ai'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 Yang Ai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang Ai more than expected).
Fields of papers citing papers by Yang Ai
This network shows the impact of papers produced by Yang Ai. 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 Yang Ai. The network helps show where Yang Ai may publish in the future.
Co-authors
The 23 scholars most cited alongside Yang Ai, 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 43 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 44 | |
| 2 | 2019 | 23 | |
| 3 | 2024 | 19 | |
| 4 | 2018 | 16 | |
| 5 | 2023 | 16 | |
| 6 | 2023 | 9 | |
| 7 | 2025 | 8 | |
| 8 | 2019 | 8 | |
| 9 | 2024 | 7 | |
| 10 | 2024 | 7 | |
| 11 | 2020 | 5 | |
| 12 | 2022 | 4 | |
| 13 | 2009 | 4 | |
| 14 | 2010 | 4 | |
| 15 | 2021 | 4 | |
| 16 | 2009 | 4 | |
| 17 | 2020 | 4 | |
| 18 | 2023 | 3 | |
| 19 | 2023 | 3 | |
| 20 | 2023 | 2 |
About Yang Ai
Yang Ai is a scholar working on Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Computational Mechanics and Experimental and Cognitive Psychology, having authored 43 papers that have together received 210 indexed citations. Recurring topics across this work include Speech and Audio Processing (32 papers), Speech Recognition and Synthesis (30 papers), Music and Audio Processing (15 papers), Neural Networks and Applications (5 papers), Advanced Data Compression Techniques (4 papers), Blind Source Separation Techniques (4 papers), Natural Language Processing Techniques (4 papers) and Advanced Adaptive Filtering Techniques (4 papers). The work is most often cited by research in Signal Processing (166 citations), Artificial Intelligence (136 citations), Computer Vision and Pattern Recognition (52 citations), Pharmacy (6 citations) and Computational Mechanics (15 citations). Yang Ai has collaborated with scholars based in China, Japan and Italy. Frequent co-authors include Zhen-Hua Ling, Li-Rong Dai, Yu Gu, Zhanshan Li, Junichi Yamagishi, Xin Wang, Kun Wang, Changhai Zhang, Ang Li and Hao Xu. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, IEEE Signal Processing Letters, Neural Networks, Lecture notes in computer science and ACM Transactions on Asian and Low-Resource Language Information Processing.
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.