World's Best Scientists 2026 revealed!
Keisuke Kinoshita

Keisuke Kinoshita

D-Index & Metrics

Computer Science

D-Index
42
Citations
7205
World Ranking
8415
National Ranking
113

Keisuke Kinoshita publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Keisuke Kinoshita sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 238 publications — 59th percentile

59% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Keisuke Kinoshita D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Keisuke Kinoshita sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 42 D-Index — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Keisuke Kinoshita is a researcher affiliated with NTT in Japan, specializing in computer science with a focus on signal processing and artificial intelligence. Their research contributions span multiple subfields including signal processing, computational mechanics, cognitive neuroscience, and mechanical engineering.

The scientist's principal topics of work include:

  • Speech and Audio Processing
  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Advanced Adaptive Filtering Techniques
  • Hearing Loss and Rehabilitation
  • Blind Source Separation Techniques
  • Microstructure and Mechanical Properties of Steels

Keisuke Kinoshita's recent publications profile includes:

  • "Neural Target Speech Extraction: An overview", 2023, IEEE Signal Processing Magazine
  • "End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend", 2021, arXiv (Cornell University)
  • "SoundBeam: Target Sound Extraction Conditioned on Sound-Class Labels and Enrollment Clues for Increased Performance and Continuous Learning", 2022, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Changes in States of Carbon and Mechanical Properties with Aging at 50°C after Quenching in Low Carbon Steel", 2020, MATERIALS TRANSACTIONS
  • "Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition", 2022, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The researcher's frequent co-authors include:

  • Marc Delcroix
  • Tomohiro Nakatani
  • Shoko Araki
  • Tsubasa Ochiai
  • Naoyuki Kamo

Keisuke Kinoshita has published extensively in venues such as:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Interspeech 2022
  • 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

The scientist's work combines elements from signal processing and artificial intelligence to address challenges in speech and audio technologies. Their research covers advanced filtering techniques and addresses practical issues such as speech enhancement, dereverberation, and beamforming.

In addition to audio and speech processing, Keisuke Kinoshita has contributed research to understanding the microstructure and mechanical properties of steels, highlighting interdisciplinary engagement with computational mechanics and materials science.

Best Publications

  • Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction

    Tomohiro Nakatani;Takuya Yoshioka;Keisuke Kinoshita;Masato Miyoshi

  • The reverb challenge: Acommon evaluation framework for dereverberation and recognition of reverberant speech

    Keisuke Kinoshita;Marc Delcroix;Takuya Yoshioka;Tomohiro Nakatani

  • A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research

    Keisuke Kinoshita;Marc Delcroix;Sharon Gannot;Emanuël A. P. Habets

  • Making Machines Understand Us in Reverberant Rooms: Robustness Against Reverberation for Automatic Speech Recognition

    Takuya Yoshioka;A. Sehr;M. Delcroix;K. Kinoshita

  • The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices

    Takuya Yoshioka;Nobutaka Ito;Marc Delcroix;Atsunori Ogawa

  • Suppression of Late Reverberation Effect on Speech Signal Using Long-Term Multiple-step Linear Prediction

    K. Kinoshita;M. Delcroix;T. Nakatani;M. Miyoshi

  • SpeakerBeam: Speaker Aware Neural Network for Target Speaker Extraction in Speech Mixtures

    Katerina Zmolikova;Marc Delcroix;Keisuke Kinoshita;Tsubasa Ochiai

  • Single Channel Target Speaker Extraction and Recognition with Speaker Beam

    Marc Delcroix;Katerina Zmolikova;Keisuke Kinoshita;Atsunori Ogawa

  • Blind speech dereverberation with multi-channel linear prediction based on short time fourier transform representation

    T. Nakatani;T. Yoshioka;K. Kinoshita;M. Miyoshi

  • Speaker-Aware Neural Network Based Beamformer for Speaker Extraction in Speech Mixtures.

    Kateřina Žmolíková;Marc Delcroix;Keisuke Kinoshita;Takuya Higuchi

  • A Multichannel MMSE-Based Framework for Speech Source Separation and Noise Reduction

    Mehrez Souden;Shoko Araki;Keisuke Kinoshita;Tomohiro Nakatani

  • Improving Speaker Discrimination of Target Speech Extraction With Time-Domain Speakerbeam

    Marc Delcroix;Tsubasa Ochiai;Katerina Zmolikova;Keisuke Kinoshita

  • Low-Latency Real-Time Meeting Recognition and Understanding Using Distant Microphones and Omni-Directional Camera

    T. Hori;S. Araki;T. Yoshioka;M. Fujimoto

  • Exploiting spectro-temporal locality in deep learning based acoustic event detection

    Miquel Espi;Masakiyo Fujimoto;Keisuke Kinoshita;Tomohiro Nakatani

  • Neural Network-Based Spectrum Estimation for Online WPE Dereverberation.

    Keisuke Kinoshita;Marc Delcroix;Haeyong Kwon;Takuma Mori

  • Neural Target Speech Extraction: An overview

    Unknown

  • Improving Noise Robust Automatic Speech Recognition with Single-Channel Time-Domain Enhancement Network

    Keisuke Kinoshita;Tsubasa Ochiai;Marc Delcroix;Tomohiro Nakatani

  • All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis

    Thilo von Neumann;Keisuke Kinoshita;Marc Delcroix;Shoko Araki

  • Harmonicity-Based Blind Dereverberation for Single-Channel Speech Signals

    T. Nakatani;K. Kinoshita;M. Miyoshi

  • A Unified Convolutional Beamformer for Simultaneous Denoising and Dereverberation

    Tomohiro Nakatani;Keisuke Kinoshita

  • Spectral Subtraction Steered by Multi-Step Forward Linear Prediction For Single Channel Speech Dereverberation

    K. Kinoshita;T. Nakatani;M. Miyoshi

  • Listening to Each Speaker One by One with Recurrent Selective Hearing Networks

    Keisuke Kinoshita;Lukas Drude;Marc Delcroix;Tomohiro Nakatani

Frequent Co-Authors

Marc Delcroix
Marc Delcroix NTT (Japan)
Shoko Araki
Shoko Araki NTT (Japan)
Takuya Yoshioka
Takuya Yoshioka Microsoft (United States)
Reinhold Haeb-Umbach
Reinhold Haeb-Umbach University of Paderborn
Walter Kellermann
Walter Kellermann University of Erlangen-Nuremberg
Shinji Watanabe
Shinji Watanabe Carnegie Mellon University
Hiroshi Sawada
Hiroshi Sawada NTT (Japan)
Yanmin Qian
Yanmin Qian Shanghai Jiao Tong University
Nobutaka Ono
Nobutaka Ono Tokyo Metropolitan University

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