World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
59
Citations
12980
World Ranking
1762
National Ranking
85

Computer Science

D-Index
65
Citations
15118
World Ranking
2479
National Ranking
140

Mark D. Plumbley publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Mark D. Plumbley sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 324 publications — 62nd percentile

62% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Mark D. Plumbley D-index placement in Electronics and Electrical Engineering in 2026

The chart shows the D-index (discipline H-index) distribution of Electronics and Electrical Engineering scientists ranked by Research.com in 2026. The highlighted bar marks where Mark D. Plumbley sits on this spectrum.

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 59 D-Index — 75th percentile

75% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Research.com Recognitions

  • 2015 - IEEE Fellow For contributions to latent variable analysis

Overview

Mark D. Plumbley is affiliated with the University of Surrey in the United Kingdom. Their research primarily focuses on fields related to computer science, with a particular concentration on signal processing and artificial intelligence. Their work spans several subfields including signal processing, artificial intelligence, computer vision and pattern recognition, developmental biology, and biomedical engineering.

Their research addresses topics centered on music and audio processing, speech and audio processing, speech recognition and synthesis, music technology and sound studies, natural language processing techniques, video analysis and summarization, and animal vocal communication and behavior.

Mark D. Plumbley has co-authored many publications with frequent collaborators such as Wenwu Wang, Haohe Liu, Xubo Liu, Qiuqiang Kong, and Xinhao Mei. These co-authors have contributed alongside Plumbley to a significant number of works in audio-related fields.

The scientist has published extensively in notable venues, including:

  • arXiv (Cornell University)
  • Zenodo (CERN European Organization for Nuclear Research)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • IEEE Transactions on Audio Speech and Language Processing
  • NOISE-CON proceedings

Selected recent works include:

  • "PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "Sound Event Detection: A tutorial," 2021, IEEE Signal Processing Magazine
  • "Sound Event Detection of Weakly Labelled Data With CNN-Transformer and Automatic Threshold Optimization," 2020, IEEE/ACM Transactions on Audio Speech and Language Processing
  • "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

Mark D. Plumbley was recognized as an IEEE Fellow in 2015 for contributions to latent variable analysis.

Best Publications

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

    Qiuqiang Kong;Yin Cao;Turab Iqbal;Yuxuan Wang

  • Best Practices for Scientific Computing

    Greg Wilson;D. A. Aruliah;C. Titus Brown;Neil P. Chue Hong

  • Detection and Classification of Acoustic Scenes and Events

    Dan Stowell;Dimitrios Giannoulis;Emmanouil Benetos;Mathieu Lagrange

  • Acoustic Scene Classification: Classifying environments from the sounds they produce

    Daniele Barchiesi;Dimitrios Giannoulis;Dan Stowell;Mark D. Plumbley

  • Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge

    Annamaria Mesaros;Toni Heittola;Emmanouil Benetos;Peter Foster

  • Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning

    Dan Stowell;Mark D. Plumbley

  • Context-Dependent Beat Tracking of Musical Audio

    M.E.P. Davies;M.D. Plumbley

  • Algorithms for nonnegative independent component analysis

    M.D. Plumbley

  • Sparse Representations in Audio and Music: From Coding to Source Separation

    Mark D Plumbley;Thomas Blumensath;Laurent Daudet;Remi Gribonval

  • Audio Inpainting

    A. Adler;V. Emiya;M. G. Jafari;M. Elad

  • Computational Analysis of Sound Scenes and Events

    Tuomas Virtanen;Mark D. Plumbley;Dan Ellis

  • Detection and classification of acoustic scenes and events: An IEEE AASP challenge

    Dimitrios Giannoulis;Emmanouil Benetos;Dan Stowell;Mathias Rossignol

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

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

  • Sound Event Detection: A tutorial

    Annamaria Mesaros;Toni Heittola;Tuomas Virtanen;Mark D. Plumbley

  • Theorems on Positive Data: on the Uniqueness of NMF

    Hans Laurberg;Mads Græsbøll Christensen;Mark D. Plumbley;Lars Kai Hansen

  • Wideband Spectrum Sensing on Real-Time Signals at Sub-Nyquist Sampling Rates in Single and Cooperative Multiple Nodes

    Zhijin Qin;Yue Gao;Mark D. Plumbley;Clive G. Parini

  • Fast Dictionary Learning for Sparse Representations of Speech Signals

    Maria G. Jafari;Mark D. Plumbley

  • Acoustic Scene Classification

    Daniele Barchiesi;Dimitrios Giannoulis;Dan Stowell;Mark D. Plumbley

  • A "nonnegative PCA" algorithm for independent component analysis

    M.D. Plumbley;E. Oja

  • Automatic music transcription and audio source separation

    Mark D. Plumbley;Samer A. Abdallah;Juan Pablo Bello;Mike E. Davies

  • SparseRepresentationsinAudio and Music: From Coding to Source Separation The fidelity of music and other audio can usually be accurately and rapidly predicted from a relatively small sample of signal information.

    Mark D. Plumbley;Thomas Blumensath;Laurent Daudet;Remi Gribonval

Frequent Co-Authors

Wenwu Wang
Wenwu Wang University of Surrey
Qiuqiang Kong
Qiuqiang Kong Chinese University of Hong Kong
Emmanuel Vincent
Emmanuel Vincent University of Lorraine
Emmanouil Benetos
Emmanouil Benetos Queen Mary University of London
Mark Sandler
Mark Sandler Google (United States)
Juan Pablo Bello
Juan Pablo Bello New York University
Rémi Gribonval
Rémi Gribonval École Normale Supérieure de Lyon
Björn Schuller
Björn Schuller Imperial College London
Michael Elad
Michael Elad Technion – Israel Institute of Technology
Anssi Klapuri
Anssi Klapuri Yousician

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