World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
39
Citations
6518
World Ranking
9764
National Ranking
146

Nobutaka Ono 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 Nobutaka Ono 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: 327 publications — 78th percentile

78% of scientists in this discipline score the same or lower.

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

Nobutaka Ono 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 Nobutaka Ono 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: 39 D-Index — 33rd percentile

33% of scientists in this discipline score the same or lower.

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

Overview

Nobutaka Ono is affiliated with Tokyo Metropolitan University in Japan and has a substantial body of research primarily in the fields of computer science and engineering. Their work focuses on signal processing, with a particular emphasis on speech and audio processing.

The main areas of study covered in Nobutaka Ono's publications include:

  • Computer Science
  • Engineering

Subfields of study include:

  • Signal Processing
  • Computational Mechanics
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Cognitive Neuroscience

Key topics addressed in their research involve:

  • Speech and Audio Processing
  • Music and Audio Processing
  • Advanced Adaptive Filtering Techniques
  • Blind Source Separation Techniques
  • Speech Recognition and Synthesis
  • Autism Spectrum Disorder Research
  • Sparse and Compressive Sensing Techniques

Nobutaka Ono has contributed to various publication venues, frequently appearing in:

  • arXiv (Cornell University)
  • 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
  • APSIPA Transactions on Signal and Information Processing
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • EURASIP Journal on Audio Speech and Music Processing

Recent papers authored or coauthored by Nobutaka Ono include:

  • "Effect of a novel nasal oxytocin spray with enhanced bioavailability on autism: a randomized trial," 2021, Brain
  • "Time-Frequency-Bin-Wise Linear Combination of Beamformers for Distortionless Signal Enhancement," 2021, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering," 2022, 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
  • "End-to-End Integration of Speech Recognition, Dereverberation, Beamforming, and Self-Supervised Learning Representation," 2023, 2022 IEEE Spoken Language Technology Workshop (SLT)
  • "MM Algorithms for Joint Independent Subspace Analysis with Application to Blind Single and Multi-Source Extraction," 2020, arXiv (Cornell University)

Frequent coauthors collaborating with Nobutaka Ono are:

  • Kouei Yamaoka
  • Yoshiki Masuyama
  • Yukoh Wakabayashi
  • Yuma Kinoshita
  • Taishi Nakashima

Best Publications

  • Stable and fast update rules for independent vector analysis based on auxiliary function technique

    Nobutaka Ono

  • Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization

    Daichi Kitamura;Nobutaka Ono;Hiroshi Sawada;Hirokazu Kameoka

  • The 2016 Signal Separation Evaluation Campaign

    Antoine Liutkus;Fabian-Robert Stöter;Zafar Rafii;Daichi Kitamura

  • Complex NMF: A new sparse representation for acoustic signals

    Hirokazu Kameoka;Nobutaka Ono;Kunio Kashino;Shigeki Sagayama

  • Separation of a monaural audio signal into harmonic/percussive components by complementary diffusion on spectrogram

    Nobutaka Ono;Kenichi Miyamoto;Jonathan Le Roux;Hirokazu Kameoka

  • Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence

    Masahiro Nakano;Hirokazu Kameoka;Jonathan Le Roux;Yu Kitano

  • Multipitch Analysis with Harmonic Nonnegative Matrix Approximation.

    Stanislaw Andrzej Raczynski;Nobutaka Ono;Shigeki Sagayama

  • Blind alignment of asynchronously recorded signals for distributed microphone array

    Nobutaka Ono;Hitoshi Kohno;Nobutaka Ito;Shigeki Sagayama

  • A REAL-TIME EQUALIZER OF HARMONIC AND PERCUSSIVE COMPONENTS IN MUSIC SIGNALS

    Nobutaka Ono;Kenichi Miyamoto;Hirokazu Kameoka;Shigeki Sagayama

  • A review of blind source separation methods: two converging routes to ILRMA originating from ICA and NMF

    Hiroshi Sawada;Nobutaka Ono;Hirokazu Kameoka;Daichi Kitamura

  • Auxiliary-function-based independent component analysis for super-Gaussian sources

    Nobutaka Ono;Shigeki Miyabe

  • Fast signal reconstruction from magnitude STFT spectrogram based on spectrogram consistency

    Jonathan Le Roux;Hirokazu Kameoka;Nobutaka Ono;Shigeki Sagayama

  • HMM-based approach for automatic chord detection using refined acoustic features

    Yushi Ueda;Yuki Uchiyama;Takuya Nishimoto;Nobutaka Ono

  • Explicit consistency constraints for STFT spectrograms and their application to phase reconstruction.

    Jonathan Le Roux;Nobutaka Ono;Shigeki Sagayama

  • The 2015 Signal Separation Evaluation Campaign

    Nobutaka Ono;Zafar Rafii;Daichi Kitamura;Nobutaka Ito

  • Sparseness-Based 2CH BSS using the EM Algorithm in Reverberant Environment

    Yosuke Izumi;Nobutaka Ono;Shigeki Sagayama

  • The 2013 Signal Separation Evaluation Campaign

    Nobutaka Ono;Zbynek Koldovsky;Shigeki Miyabe;Nobutaka Ito

  • Melody line estimation in homophonic music audio signals based on temporal-variability of melodic source

    Hideyuki Tachibana;Takuma Ono;Nobutaka Ono;Shigeki Sagayama

  • Voice liveness detection algorithms based on pop noise caused by human breath for automatic speaker verification

    Sayaka Shiota;Fernando Villavicencio;Junichi Yamagishi;Nobutaka Ono

  • Deeply Learned Filter Response Functions for Hyperspectral Reconstruction

    Shijie Nie;Lin Gu;Yinqiang Zheng;Antony Lam

  • Determined Blind Source Separation with Independent Low-Rank Matrix Analysis

    Daichi Kitamura;Nobutaka Ono;Hiroshi Sawada;Hirokazu Kameoka

Frequent Co-Authors

Shigeki Sagayama
Shigeki Sagayama University of Tokyo
Hirokazu Kameoka
Hirokazu Kameoka NTT (Japan)
Shoji Makino
Shoji Makino Waseda University
Hiroshi Saruwatari
Hiroshi Saruwatari University of Tokyo
Jonathan Le Roux
Jonathan Le Roux Mitsubishi Electric (United States)
Emmanuel Vincent
Emmanuel Vincent University of Lorraine
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics
Hiroshi Sawada
Hiroshi Sawada NTT (Japan)
Shoko Araki
Shoko Araki NTT (Japan)

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