Daiki Takeuchi

626 citations
29 papers · 306 indexed · h-index 10

Impact in

Papers in

    • Speech and Audio Processing 18
    • Music and Audio Processing 15
    • Diverse Musicological Studies 3

Daiki Takeuchi

26 papers receiving 287 citations

Peers

Daiki Takeuchi
Comparison fields: 5 of 64
  • Signal Processing 223
  • Artificial Intelligence 136
  • Computational Mechanics 53
  • Music 6
  • Computer Vision and Pattern Recognition 39
Replace Miquel Espi with:
Miquel Espi Japan
Sherif Abdulatif Germany
Xiaojia Zhao United States
Éric Plourde Canada
Dongchao Yang China
Daniel Michelsanti Denmark
Jiří Málek Czechia
Aswin Shanmugam Subramanian United States
Biing-Hwang Juang United States
Zhengkun Tian China
Daiki Takeuchi relative to Miquel Espi Japan Miquel Espi's profile →
Citations per field
00.5×10×15.8×
Miquel Espi · 1×
Citations per year

Countries citing papers authored by Daiki Takeuchi

Since Specialization
Citations

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

Fields of papers citing papers by Daiki Takeuchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Daiki Takeuchi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daiki Takeuchi Line = papers co-authored together Daiki Takeuchi links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20249
3 20241
4 20241
5 20244
6 202422
7 20241
8 20244
9 202316
10 20232
11 20232
12 20230
13 202231
14 202212
15 20228
16 202033
17 202086
18 20206
19 20184
20 20153

About Daiki Takeuchi

Daiki Takeuchi is a scholar working on Signal Processing, Music, Artificial Intelligence, Computational Mechanics and Cognitive Neuroscience, having authored 29 papers that have together received 306 indexed citations. Recurring topics across this work include Speech and Audio Processing (18 papers), Music and Audio Processing (15 papers), Speech Recognition and Synthesis (10 papers), Advanced Adaptive Filtering Techniques (4 papers), Hearing Loss and Rehabilitation (3 papers), Diverse Musicological Studies (3 papers), Natural Language Processing Techniques (3 papers) and Photonic and Optical Devices (2 papers). The work is most often cited by research in Signal Processing (223 citations), Artificial Intelligence (136 citations), Computational Mechanics (53 citations), Music (6 citations) and Computer Vision and Pattern Recognition (39 citations). Daiki Takeuchi has collaborated with scholars based in Japan and China. Frequent co-authors include Kohei Yatabe, Noboru Harada, Yuma Koizumi, Yasunori Ohishi, Marc Delcroix, Yasuhiro Oikawa, Yoshiki Masuyama, Kunio Kashino, Masahiro Yasuda and Makoto Kinoshita. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Optics Express, Cell Genomics, IEICE Transactions on Electronics and The Journal of the Acoustical Society of America.

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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