World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
8502
World Ranking
7550
National Ranking
449

Wenwu Wang 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 Wenwu Wang 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: 387 publications — 86th percentile

86% of scientists in this discipline score the same or lower.

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

Wenwu Wang 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 Wenwu Wang 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: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

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

Overview

Wenwu Wang is affiliated with the University of Surrey in the United Kingdom. Their primary research areas are within Computer Science, with a significant focus on Signal Processing, Artificial Intelligence, and Computer Vision and Pattern Recognition. Additional subfields include Electrical and Electronic Engineering and Computational Mechanics.

The main topics of Wenwu Wang's work encompass:

  • Music and Audio Processing
  • Speech and Audio Processing
  • Speech Recognition and Synthesis
  • Music Technology and Sound Studies
  • Video Analysis and Summarization
  • Anomaly Detection Techniques and Applications
  • Multimodal Machine Learning Applications

Wenwu Wang has contributed extensively to academic literature, with numerous publications appearing predominantly in the following venues:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • IEEE Signal Processing Letters
  • Zenodo (CERN European Organization for Nuclear Research)
  • 2022 30th European Signal Processing Conference (EUSIPCO)

Among recent papers authored or co-authored by Wenwu Wang are:

  • "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Plastic damage prediction of concrete under compression based on deep learning," 2023, Acta Mechanica
  • "AudioLDM 2: Learning Holistic Audio Generation With Self-Supervised Pretraining," 2024, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "WavCaps: A ChatGPT-Assisted Weakly-Labelled Audio Captioning Dataset for Audio-Language Multimodal Research," 2024, IEEE/ACM Transactions on Audio Speech and Language Processing

Wenwu Wang's frequent collaborators include:

  • Mark D. Plumbley
  • Xubo Liu
  • Haohe Liu
  • Xinhao Mei
  • Qiuqiang Kong

The collective body of Wenwu Wang's research highlights a consistent focus on advancing technologies in audio and speech processing, machine learning applications in multimodal data, and signal processing methodologies alongside contributions to computational mechanics through deep learning approaches.

Best Publications

  • PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition

    Qiuqiang Kong;Yin Cao;Turab Iqbal;Yuxuan Wang

  • Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network

    Yong Xu;Qiuqiang Kong;Wenwu Wang;Mark D. Plumbley

  • AudioLDM: Text-to-Audio Generation with Latent Diffusion Models

    Unknown

  • Simultaneous Codeword Optimization (SimCO) for Dictionary Update and Learning

    Wei Dai;Tao Xu;Wenwu Wang

  • Sound Event Detection and Time–Frequency Segmentation from Weakly Labelled Data

    Qiuqiang Kong;Yong Xu;Iwona Sobieraj;Wenwu Wang

  • Heterogeneous Feature Selection With Multi-Modal Deep Neural Networks and Sparse Group LASSO

    Lei Zhao;Qinghua Hu;Wenwu Wang

  • Blind Source Separation

    Ganesh R. Naik;Wenwu Wang

  • Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization

    Qiuqiang Kong;Yong Xu;Wenwu Wang;Mark D. Plumbley

  • Polyphonic sound event detection and localization using a two-stage strategy

    Yin Cao;Qiuqiang Kong;Turab Iqbal;Fengyan An

  • Audio Set Classification with Attention Model: A Probabilistic Perspective

    Qiuqiang Kong;Yong Xu;Wenwu Wang;Mark D. Plumbley

  • Anomalous Sound Detection Using Spectral-Temporal Information Fusion

    Unknown

  • Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

    Yong Xu;Qiang Huang;Wenwu Wang;Peter Foster

  • Convolutional gated recurrent neural network incorporating spatial features for audio tagging

    Yong Xu;Qiuqiang Kong;Qiang Huang;Wenwu Wang

  • Penalty function-based joint diagonalization approach for convolutive blind separation of nonstationary sources

    Wenwu Wang;S. Sanei;J.A. Chambers

  • Audio Assisted Robust Visual Tracking With Adaptive Particle Filtering

    Volkan Kilic;Mark Barnard;Wenwu Wang;Josef Kittler

  • Audiovisual Speech Source Separation: An overview of key methodologies

    Bertrand Rivet;Wenwu Wang;Syed Mohsen Naqvi;Jonathon A. Chambers

  • Video assisted speech source separation

    Wenwu Wang;D. Cosker;Y. Hicks;S. Saneit

  • Tensor dictionary learning with sparse TUCKER decomposition

    Syed Zubair;Wenwu Wang

  • Deep Neural Network Baseline for DCASE Challenge 2016

    Qiuqiang Kong;Iwona Sobieraj;Wenwu Wang;Mark Plumbley

  • A Multiplicative Algorithm for Convolutive Non-Negative Matrix Factorization Based on Squared Euclidean Distance

    Wenwu Wang;A. Cichocki;J.A. Chambers

  • Machine Audition: Principles, Algorithms and Systems

    Wenwu Wang

  • Variable step-size sign natural gradient algorithm for sequential blind source separation

    Lianxi Yuan;Wenwu Wang;J.A. Chambers

  • Weakly Labelled AudioSet Tagging with Attention Neural Networks

    Qiuqiang Kong;Changsong Yu;Turab Iqbal;Yong Xu

Frequent Co-Authors

Mark D. Plumbley
Mark D. Plumbley King's College London
Jonathon A. Chambers
Jonathon A. Chambers Harbin Engineering University
Qiuqiang Kong
Qiuqiang Kong Chinese University of Hong Kong
Saeid Sanei
Saeid Sanei Nottingham Trent University
Josef Kittler
Josef Kittler University of Surrey
Yuexian Zou
Yuexian Zou Peking University
Adrian Hilton
Adrian Hilton University of Surrey
Yuxuan Wang
Yuxuan Wang ByteDance
Qinghua Hu
Qinghua Hu Tianjin University
Francis Bach
Francis Bach École Normale Supérieure

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