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D-Index & Metrics

Computer Science

D-Index
49
Citations
9724
World Ranking
5885
National Ranking
352

Simon Dixon 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 Simon Dixon 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: 219 publications — 53rd percentile

53% of scientists in this discipline score the same or lower.

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

Simon Dixon 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 Simon Dixon 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: 49 D-Index — 60th percentile

60% of scientists in this discipline score the same or lower.

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

Overview

Simon Dixon is affiliated with Queen Mary University of London in the United Kingdom. Their research focuses primarily within the field of Computer Science, with significant contributions to subfields including Signal Processing, Computer Vision and Pattern Recognition, Music, Artificial Intelligence, and Cognitive Neuroscience.

Their work covers a range of topics related to music and audio processing, music technology and sound studies, diverse musicological studies, speech recognition and synthesis, natural language processing techniques, speech and audio processing, and neuroscience and music perception.

Simon Dixon has published extensively in several venues, notably:

  • Zenodo (CERN European Organization for Nuclear Research)
  • arXiv (Cornell University)
  • Transactions of the International Society for Music Information Retrieval
  • IEEE Signal Processing Letters
  • Applied Sciences

Their recent papers include:

  • PiJAMA: Piano Jazz with Automatic MIDI Annotations, 2023, Transactions of the International Society for Music Information Retrieval
  • A Data-Driven Analysis of Robust Automatic Piano Transcription, 2024, IEEE Signal Processing Letters
  • DExter: Learning and Controlling Performance Expression with Diffusion Models, 2024, Applied Sciences
  • The Jazz Ontology: A semantic model and large-scale RDF repositories for jazz, 2022, Journal of Web Semantics
  • A Convolutional-Attentional Neural Framework for Structure-Aware Performance-Score Synchronization, 2021, IEEE Signal Processing Letters

Simon Dixon frequently collaborates with other researchers in the field. Their notable coauthors include:

  • Emmanouil Benetos
  • Sungkyun Chang
  • Drew Edwards
  • Xavier Riley
  • Yixiao Zhang

Their publications indicate a sustained focus on music and audio-oriented computation methods, especially those involving automatic transcription, performance expression modeling, structure-aware synchronization, and semantic modeling in musical contexts.

Best Publications

  • Automatic Extraction of Tempo and Beat From Expressive Performances

    Simon Dixon

  • Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation

    Daniel Stoller;Sebastian Ewert;Simon Dixon

  • PYIN: A fundamental frequency estimator using probabilistic threshold distributions

    Matthias Mauch;Simon Dixon

  • Automatic music transcription: challenges and future directions

    Emmanouil Benetos;Simon Dixon;Dimitrios Giannoulis;Holger Kirchhoff

  • An end-to-end neural network for polyphonic piano music transcription

    Siddharth Sigtia;Emmanouil Benetos;Simon Dixon

  • An experimental comparison of audio tempo induction algorithms

    F. Gouyon;A. Klapuri;S. Dixon;M. Alonso

  • Automatic Music Transcription: An Overview

    Emmanouil Benetos;Simon Dixon;Zhiyao Duan;Sebastian Ewert

  • A Review of Automatic Rhythm Description Systems

    Fabien Gouyon;Simon Dixon

  • Evaluation of the Audio Beat Tracking System BeatRoot

    Simon Dixon

  • APPROXIMATE NOTE TRANSCRIPTION FOR THE IMPROVED IDENTIFICATION OF DIFFICULT CHORDS

    Matthias Mauch;Simon Dixon

  • A Survey of Music Recommendation Systems and Future Perspectives

    Yading Song;Simon Dixon;Marcus Pearce

  • MATCH: A Music Alignment Tool Chest

    Simon Dixon;Gerhard Widmer

  • Exploring Music Collections by Browsing Different Views

    Elias Pampalk;Simon Dixon;Gerhard Widmer;Gerhard Widmer

  • Evaluating Rhythmic descriptors for Musical Genre Classification

    Simon Dixon;Elias Pampalk;Gerhard Widmer

  • Simultaneous Estimation of Chords and Musical Context From Audio

    M Mauch;S Dixon

  • Towards Characterisation of Music via Rhythmic Patterns

    Simon Dixon;Fabien Gouyon;Gerhard Widmer

  • In search of the Horowitz factor

    Gerhard Widmer;Simon Dixon;Werner Goebl;Elias Pampalk

  • Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency

    M Mauch;C Cannam;R Bittner;G Fazekas

  • Classification of Dance Music by Periodicity Patterns

    Simon Dixon;Elias Pampalk;Gerhard Widmer

  • LIVE TRACKING OF MUSICAL PERFORMANCES USING ON-LINE TIME WARPING

    Simon Dixon

  • Improved music feature learning with deep neural networks

    Siddharth Sigtia;Simon Dixon

Frequent Co-Authors

Emmanouil Benetos
Emmanouil Benetos Queen Mary University of London
Gerhard Widmer
Gerhard Widmer Johannes Kepler University of Linz
Mark Sandler
Mark Sandler Google (United States)
Anssi Klapuri
Anssi Klapuri Yousician
Marcus T. Pearce
Marcus T. Pearce Queen Mary University of London
Artur S. d'Avila Garcez
Artur S. d'Avila Garcez City, University of London
Mark D. Plumbley
Mark D. Plumbley King's College London
Andrea R. Halpern
Andrea R. Halpern Bucknell University
Sean Bechhofer
Sean Bechhofer University of Manchester
Juan Pablo Bello
Juan Pablo Bello New York University

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