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Hiroshi Sawada

Hiroshi Sawada

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Computer Science
Japan
2025

D-Index & Metrics

Computer Science

D-Index
51
Citations
9795
World Ranking
5370
National Ranking
67

Hiroshi Sawada 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 Hiroshi Sawada 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: 252 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Hiroshi Sawada 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 Hiroshi Sawada 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: 51 D-Index — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Research.com Recognitions

  • 2025 - Research.com Computer Science in Japan Leader Award
  • 2022 - Research.com Computer Science in Japan Leader Award

Overview

Hiroshi Sawada is affiliated with NTT in Japan and has made contributions primarily in the fields of Computer Science and Engineering. Their research spans various subfields including Signal Processing, Electrical and Electronic Engineering, Computational Mechanics, Materials Chemistry, and Artificial Intelligence.

The scientist's expertise encompasses multiple topics such as Speech and Audio Processing, Blind Source Separation Techniques, Advanced Adaptive Filtering Techniques, Optical Network Technologies, Electronic and Structural Properties of Oxides, Neural Networks, and Reservoir Computing, and Photonic and Optical Devices.

Frequent collaborators of Hiroshi Sawada include Rintaro Ikeshita, Tomohiro Nakatani, Takuma Otsuka, Keisuke Kinoshita, and Kazuo Aoyama.

Regarding publication venues, the most frequent sites for their work include:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • NTT technical review
  • npj Computational Materials
  • Proceedings of the AAAI Conference on Artificial Intelligence

Some of the recent papers authored or co-authored by Hiroshi Sawada are:

  • Bayesian optimization with experimental failure for high-throughput materials growth, 2022, npj Computational Materials
  • A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter, 2021, IEEE/ACM Transactions on Audio Speech and Language Processing
  • Switching Independent Vector Analysis and its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms, 2022, IEEE/ACM Transactions on Audio Speech and Language Processing
  • Bayesian Unification of Sound Source Localization and Separation with Permutation Resolution, 2021, Proceedings of the AAAI Conference on Artificial Intelligence
  • Intrinsic physics in magnetic Weyl semimetal SrRuO3 films addressed by machine-learning-assisted molecular beam epitaxy, 2022, Japanese Journal of Applied Physics

Best Publications

  • A robust and precise method for solving the permutation problem of frequency-domain blind source separation

    H. Sawada;R. Mukai;S. Araki;S. Makino

  • Blind speech separation

    Shoji Makino;Hiroshi Sawada;Te-Won Lee

  • Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment

    Hiroshi Sawada;Shoko Araki;Shoji Makino

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

    Daichi Kitamura;Nobutaka Ono;Hiroshi Sawada;Hirokazu Kameoka

  • Minimization of binary decision diagrams based on exchanges of variables

    N. Ishiura;H. Sawada;S. Yajima

  • Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

    H. Sawada;H. Kameoka;S. Araki;N. Ueda

  • Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors

    Shoko Araki;Hiroshi Sawada;Ryo Mukai;Shoji Makino

  • First stereo audio source separation evaluation campaign: data, algorithms and results

    Emmanuel Vincent;Hiroshi Sawada;Pau Bofill;Shoji Makino

  • Polar coordinate based nonlinear function for frequency-domain blind source separation

    Hiroshi Sawada;Ryo Mukai;Shoko Araki;Shoji Makino

  • The signal separation evaluation campaign (2007-2010): Achievements and remaining challenges

    Emmanuel Vincent;Shoko Araki;Fabian Theis;Guido Nolte

  • Grouping Separated Frequency Components by Estimating Propagation Model Parameters in Frequency-Domain Blind Source Separation

    H. Sawada;S. Araki;R. Mukai;S. Makino

  • The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation

    Shoko Araki;Alexey Ozerov;Vikrham Gowreesunker;Hiroshi Sawada

  • Measuring Dependence of Bin-wise Separated Signals for Permutation Alignment in Frequency-domain BSS

    H. Sawada;S. Araki;S. Makino

  • Map-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and l 1 -norm minimization

    Stefan Winter;Walter Kellermann;Hiroshi Sawada;Shoji Makino

  • Blind Extraction of Dominant Target Sources Using ICA and Time-Frequency Masking

    H. Sawada;S. Araki;R. Mukai;S. Makino

  • Frequency-Domain Blind Source Separation

    Shoji Makino;Hiroshi Sawada;Shoko Araki

  • Natural gradient multichannel blind deconvolution and speech separation using causal FIR filters

    S.C. Douglas;H. Sawada;S. Makino

  • 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

  • Automatic inference of cross-modal nonverbal interactions in multiparty conversations: "who responds to whom, when, and how?" from gaze, head gestures, and utterances

    Kazuhiro Otsuka;Hiroshi Sawada;Junji Yamato

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

    Mehrez Souden;Shoko Araki;Keisuke Kinoshita;Tomohiro Nakatani

  • Spatio–Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures

    S.C. Douglas;M. Gupta;H. Sawada;S. Makino

  • Fashion coordinates recommender system using photographs from fashion magazines

    Tomoharu Iwata;Shinji Watanabe;Hiroshi Sawada

Frequent Co-Authors

Shoji Makino
Shoji Makino Waseda University
Shoko Araki
Shoko Araki NTT (Japan)
Hirokazu Kameoka
Hirokazu Kameoka NTT (Japan)
Hiroshi Saruwatari
Hiroshi Saruwatari University of Tokyo
Scott C. Douglas
Scott C. Douglas Southern Methodist University
Makoto Yokoo
Makoto Yokoo Kyushu University
Nobutaka Ono
Nobutaka Ono Tokyo Metropolitan University

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