Ryuki Tachibana
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Control and Systems Engineering top 5%
- Signal Processing top 5%
- Biomedical Engineering
- Co-authors
- Giovanni De MagistrisAsim MunawarTadanobu InoueTu-Hoa PhamSeiji KobayashiMasafumi NishimuraNoboru BabaguchiMasayuki Suzuki
- Topics
- Music and Audio Processing (20 papers)Speech and Audio Processing (13 papers)Advanced Steganography and Watermarking Techniques (11 papers)
- Partner nations
- JapanUnited StatesAustria
In The Last Decade
Ryuki Tachibana
33 papers receiving 594 citations
Peers
Comparison fields: 5 of 80
- Computer Vision and Pattern Recognition 209
- Artificial Intelligence 203
- Control and Systems Engineering 202
- Signal Processing 146
- Biomedical Engineering 90
Countries citing papers authored by Ryuki Tachibana
This map shows the geographic impact of Ryuki Tachibana'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 Ryuki Tachibana with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ryuki Tachibana more than expected).
Fields of papers citing papers by Ryuki Tachibana
This network shows the impact of papers produced by Ryuki Tachibana. 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 Ryuki Tachibana. The network helps show where Ryuki Tachibana may publish in the future.
Co-authorship network of co-authors of Ryuki Tachibana
This figure shows the co-authorship network connecting the top 25 collaborators of Ryuki Tachibana. A scholar is included among the top collaborators of Ryuki Tachibana based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ryuki Tachibana. Ryuki Tachibana is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 22 | |
| 2 | 13 | |
| 3 | 0 | |
| 4 | 8 | |
| 5 | 70 | |
| 6 | 98 | |
| 7 | 203 | |
| 8 | 4 | |
| 9 | 3 | |
| 10 | 9 | |
| 11 | 0 | |
| 12 | A METRIC FOR EVALUATING SPEECH RECOGNITION ACCURACY BASED ON HUMAN PERCEPTION | 1 |
| 13 | A study on audio information hiding and its criteria for evaluation | 1 |
| 14 | 1 | |
| 15 | F0 Gradient Model for Acoustic Quality and F0 Consistency of Concatenative TTS | 0 |
| 16 | 6 | |
| 17 | 9 | |
| 18 | Capacity Analysis of Audio Watermarking Based on Logarithmic Amplitude Modification against Additive Noise | 2 |
| 19 | 43 | |
| 20 | 28 |
About Ryuki Tachibana
Ryuki Tachibana is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 37 papers that have together received 637 indexed citations. Recurring topics across this work include Music and Audio Processing (20 papers), Speech and Audio Processing (13 papers) and Advanced Steganography and Watermarking Techniques (11 papers). The work is most often cited by research in Signal Processing (146 citations), Computer Vision and Pattern Recognition (209 citations) and Control and Systems Engineering (202 citations). Ryuki Tachibana has collaborated with scholars based in Japan, United States and Austria. Frequent co-authors include Giovanni De Magistris, Asim Munawar, Tadanobu Inoue, Tu-Hoa Pham, Seiji Kobayashi, Masafumi Nishimura, Noboru Babaguchi, Masayuki Suzuki, Yuta Nakashima and Hirokazu Tachikawa. Their work appears in journals such as Journal of Affective Disorders, Signal Processing and IEEE Transactions on Multimedia.
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.