Natsuki Ueno
Impact in
- Signal Processing top 5%
- Speech and Audio Processing
- Cognitive Neuroscience top 10%
- Hearing Loss and Rehabilitation
Papers in
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- Speech and Audio Processing 20
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- Acoustic Wave Phenomena Research 21
- Acoustic Wave Resonator Technologies 4
- Co-authors
- Shoichi KoyamaHiroshi SaruwatariMakoto KanekoMikhail SvininMorito AkiyamaToshio OkadaKeiko NishikuboMasayoshi Tsubai
In The Last Decade
Natsuki Ueno
50 papers receiving 534 citations
Peers
Comparison fields: 5 of 54
- Signal Processing 229
- Cognitive Neuroscience 138
- Computational Mechanics 129
- Biomedical Engineering 261
- Computer Vision and Pattern Recognition 65
Countries citing papers authored by Natsuki Ueno
This map shows the geographic impact of Natsuki Ueno'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 Natsuki Ueno with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natsuki Ueno more than expected).
Fields of papers citing papers by Natsuki Ueno
This network shows the impact of papers produced by Natsuki Ueno. 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 Natsuki Ueno. The network helps show where Natsuki Ueno may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Natsuki Ueno, 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 | 0 | |
| 2 | 2024 | 10 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 4 | |
| 5 | 2022 | 10 | |
| 6 | 2022 | 13 | |
| 7 | 2022 | 6 | |
| 8 | 2022 | 0 | |
| 9 | 2021 | 18 | |
| 10 | 2021 | 28 | |
| 11 | 2021 | 1 | |
| 12 | 2021 | 3 | |
| 13 | 2019 | 29 | |
| 14 | 2019 | 8 | |
| 15 | 2017 | 63 | |
| 16 | 2003 | 2 | |
| 17 | 2002 | 2 | |
| 18 | Novel cell-AGC technique for burst-mode CMOS preamplifier with wide dynamic range and high sensitivity for ATM-PON system | 2001 | 1 |
| 19 | 1988 | 12 | |
| 20 | 1981 | 3 |
About Natsuki Ueno
Natsuki Ueno is a scholar working on Signal Processing, Biomedical Engineering, Computational Mechanics, Cognitive Neuroscience and Statistics, Probability and Uncertainty, having authored 55 papers that have together received 562 indexed citations. Recurring topics across this work include Acoustic Wave Phenomena Research (21 papers), Speech and Audio Processing (20 papers), Hearing Loss and Rehabilitation (9 papers), Advanced Adaptive Filtering Techniques (8 papers), Acoustic Wave Resonator Technologies (4 papers), Aerodynamics and Acoustics in Jet Flows (4 papers), Structural Health Monitoring Techniques (4 papers) and Dynamics and Control of Mechanical Systems (4 papers). The work is most often cited by research in Signal Processing (229 citations), Cognitive Neuroscience (138 citations), Computational Mechanics (129 citations), Biomedical Engineering (261 citations) and Computer Vision and Pattern Recognition (65 citations). Natsuki Ueno has collaborated with scholars based in Japan, Germany and France. Frequent co-authors include Shoichi Koyama, Hiroshi Saruwatari, Makoto Kaneko, Mikhail Svinin, Morito Akiyama, Toshio Okada, Keiko Nishikubo, Masayoshi Tsubai, Osamu Fukuda and Toshihiro Kamohara. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Scientific Reports, Journal of Materials Science, IEEE Transactions on Signal Processing and IEEE Signal Processing Letters.
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.