Takuya Nishimoto

116 total papers · 686 total citations
60 papers, 380 citations indexed

About

Takuya Nishimoto is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Takuya Nishimoto has authored 60 papers receiving a total of 380 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Signal Processing, 30 papers in Artificial Intelligence and 23 papers in Computer Vision and Pattern Recognition. Recurrent topics in Takuya Nishimoto's work include Speech and Audio Processing (30 papers), Music and Audio Processing (21 papers) and Music Technology and Sound Studies (16 papers). Takuya Nishimoto is often cited by papers focused on Speech and Audio Processing (30 papers), Music and Audio Processing (21 papers) and Music Technology and Sound Studies (16 papers). Takuya Nishimoto collaborates with scholars based in Japan, France and Germany. Takuya Nishimoto's co-authors include Shigeki Sagayama, Hirokazu Kameoka, Nobutaka Ono, Yuki Uchiyama, Shinji Sako, Masahiro Araki, Stanisław Raczyński, Satoru Fukayama, Jun Wu and Emmanuel Vincent and has published in prestigious journals such as IEEE Journal of Selected Topics in Signal Processing, IEEE Transactions on Audio Speech and Language Processing and Conference proceedings.

In The Last Decade

Takuya Nishimoto

56 papers receiving 348 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Takuya Nishimoto 300 197 99 35 30 60 380
Romain Hennequin 216 0.7× 110 0.6× 131 1.3× 29 0.8× 24 0.8× 29 349
Weiping Tu 115 0.4× 138 0.7× 195 2.0× 33 0.9× 30 1.0× 62 387
Baocai Yin 140 0.5× 189 1.0× 106 1.1× 35 1.0× 18 0.6× 56 335
Haohe Liu 264 0.9× 139 0.7× 181 1.8× 5 0.1× 22 0.7× 35 419
Shuai Nie 170 0.6× 148 0.8× 174 1.8× 47 1.3× 32 1.1× 25 342
Akira Kurematsu 240 0.8× 114 0.6× 211 2.1× 42 1.2× 13 0.4× 36 382
Thomas Pellegrini 158 0.5× 54 0.3× 237 2.4× 21 0.6× 14 0.5× 49 366
Zejun Ma 284 0.9× 101 0.5× 269 2.7× 8 0.2× 11 0.4× 42 415
Børge Lindberg 303 1.0× 81 0.4× 331 3.3× 33 0.9× 20 0.7× 40 420
Jordi Janer 274 0.9× 177 0.9× 48 0.5× 17 0.5× 99 3.3× 43 333

Countries citing papers authored by Takuya Nishimoto

Since Specialization
Citations

This map shows the geographic impact of Takuya Nishimoto'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 Takuya Nishimoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Takuya Nishimoto more than expected).

Fields of papers citing papers by Takuya Nishimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Takuya Nishimoto. 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 Takuya Nishimoto. The network helps show where Takuya Nishimoto may publish in the future.

Co-authorship network of co-authors of Takuya Nishimoto

This figure shows the co-authorship network connecting the top 25 collaborators of Takuya Nishimoto. A scholar is included among the top collaborators of Takuya Nishimoto 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 Takuya Nishimoto. Takuya Nishimoto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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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.

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