Keisuke Imoto
Impact in
- Signal Processing top 2%
- Music and Audio Processing
- Speech and Audio Processing
- Time Series Analysis and Forecasting
- Developmental Biology top 5%
- Animal Vocal Communication and Behavior
Papers in
-
- Music and Audio Processing 45
- Speech and Audio Processing 41
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- Music Technology and Sound Studies 24
- Video Analysis and Summarization 2
- Co-authors
- Hisashi Uematsu (3 shared papers)Nobutaka Ono (11 shared papers)Yuma Koizumi (2 shared papers)Noboru Harada (3 shared papers)Shoichiro Saito (1 shared paper)Suehiro Shimauchi (2 shared papers)Yoichi Yamashita (6 shared papers)Masahiro Yasuda (2 shared papers)
In The Last Decade
Keisuke Imoto
40 papers receiving 386 citations
Peers
Comparison fields: 5 of 34
- Signal Processing 332
- Developmental Biology 45
- Computer Vision and Pattern Recognition 151
- Artificial Intelligence 142
- Music 13
Countries citing papers authored by Keisuke Imoto
This map shows the geographic impact of Keisuke Imoto'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 Keisuke Imoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Keisuke Imoto more than expected).
Fields of papers citing papers by Keisuke Imoto
This network shows the impact of papers produced by Keisuke Imoto. 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 Keisuke Imoto. The network helps show where Keisuke Imoto may publish in the future.
Co-authors
The 25 scholars most cited alongside Keisuke Imoto, 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 47 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 110 | |
| 2 | 2018 | 31 | |
| 3 | 2020 | 30 | |
| 4 | 2017 | 23 | |
| 5 | 2016 | 22 | |
| 6 | 2013 | 19 | |
| 7 | 2013 | 16 | |
| 8 | 2021 | 12 | |
| 9 | 2018 | 11 | |
| 10 | 2019 | 10 | |
| 11 | 2020 | 9 | |
| 12 | 2021 | 9 | |
| 13 | 2022 | 7 | |
| 14 | 2015 | 7 | |
| 15 | 2015 | 7 | |
| 16 | 2022 | 6 | |
| 17 | 2022 | 6 | |
| 18 | 2024 | 5 | |
| 19 | 2022 | 5 | |
| 20 | 2017 | 5 |
About Keisuke Imoto
Keisuke Imoto is a scholar working on Signal Processing, Computer Vision and Pattern Recognition, Artificial Intelligence, Developmental Biology and Automotive Engineering, having authored 47 papers that have together received 395 indexed citations. Recurring topics across this work include Music and Audio Processing (45 papers), Speech and Audio Processing (41 papers), Music Technology and Sound Studies (24 papers), Speech Recognition and Synthesis (6 papers), Animal Vocal Communication and Behavior (5 papers), Anomaly Detection Techniques and Applications (3 papers), Natural Language Processing Techniques (2 papers) and Video Analysis and Summarization (2 papers). The work is most often cited by research in Signal Processing (332 citations), Developmental Biology (45 citations), Computer Vision and Pattern Recognition (151 citations), Artificial Intelligence (142 citations) and Music (13 citations). Keisuke Imoto has collaborated with scholars based in Japan, Germany and France. Frequent co-authors include Hisashi Uematsu, Nobutaka Ono, Yuma Koizumi, Noboru Harada, Shoichiro Saito, Suehiro Shimauchi, Yoichi Yamashita, Masahiro Yasuda, Yasunori Ohishi and Tatsuya Komatsu. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Applied Acoustics, IEEE Signal Processing Letters, IEICE Transactions on Information and Systems and Nippon Onkyo Gakkaishi/Acoustical science and technology/Nihon Onkyo Gakkaishi.
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.