World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
64
Citations
30562
World Ranking
2519
National Ranking
1258

Malcolm Slaney 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 Malcolm Slaney 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: 205 publications — 48th percentile

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

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

Malcolm Slaney 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 Malcolm Slaney 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: 64 D-Index — 82nd percentile

82% 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

  • 2018 - ACM Senior Member

Overview

Malcolm Slaney is affiliated with Stanford University in the United States. Their research primarily spans the fields of Computer Science and Neuroscience, with significant focus on Cognitive Neuroscience and Signal Processing as specialized subfields.

The main topics addressed in their work include:

  • Hearing Loss and Rehabilitation
  • Speech and Audio Processing
  • Music and Audio Processing
  • Neural dynamics and brain function
  • Noise Effects and Management
  • EEG and Brain-Computer Interfaces
  • Speech Recognition and Synthesis

Malcolm Slaney has contributed multiple publications to several frequent academic venues, notably:

  • arXiv (Cornell University)
  • Ear and Hearing
  • Journal of Neural Engineering
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Neural Computing and Applications

Their recent published papers include:

  • "The Quest for Ecological Validity in Hearing Science: What It Is, Why It Matters, and How to Advance It," 2020, in Ear and Hearing
  • "Auditory stimulus-response modeling with a match-mismatch task," 2021, in Journal of Neural Engineering
  • "Auditory Measures for the Next Billion Users," 2020, in Ear and Hearing
  • "Neural architecture search for energy-efficient always-on audio machine learning," 2023, in Neural Computing and Applications
  • "Real-time control of a hearing instrument with EEG-based attention decoding," 2025, in Journal of Neural Engineering

Frequent collaborators in their work include:

  • Simon Carlile
  • Alain de Cheveigné
  • Søren A. Fuglsang
  • Jens Hjortkjær
  • Richard F. Lyon

In 2018, Malcolm Slaney was recognized as an ACM Senior Member. Their work encompasses research in auditory systems, neural modeling, and brain-computer interface applications related to hearing technologies.

Best Publications

  • Principles of Computerized Tomographic Imaging

    Avinash C. Kak;Malcolm Slaney;Ge Wang

  • CNN architectures for large-scale audio classification

    Shawn Hershey;Sourish Chaudhuri;Daniel P. W. Ellis;Jort F. Gemmeke

  • Construction and evaluation of a robust multifeature speech/music discriminator

    E. Scheirer;M. Slaney

  • Content-Based Music Information Retrieval: Current Directions and Future Challenges

    M.A. Casey;R. Veltkamp;M. Goto;M. Leman

  • Video Rewrite: driving visual speech with audio

    Christoph Bregler;Michele Covell;Malcolm Slaney

  • Attentional Selection in a Cocktail Party Environment Can Be Decoded from Single-Trial EEG

    James A. O'Sullivan;Alan J. Power;Nima Mesgarani;Siddharth Rajaram

  • An Efficient Implementation of the Patterson-Holdsworth Auditory Filter Bank

    Malcolm Slaney

  • Limitations of Imaging with First-Order Diffraction Tomography

    M. Slaney;A.C. Kak;L.E. Larsen

  • Locality-Sensitive Hashing for Finding Nearest Neighbors [Lecture Notes]

    M. Slaney;M. Casey

  • An Introduction to Computational Networks and the Computational Network Toolkit

    Dong Yu;Adam Eversole;Mike Seltzer;Kaisheng Yao

  • Discrimination of speech from nonspeech based on multiscale spectro-temporal Modulations

    N. Mesgarani;M. Slaney;S.A. Shamma

  • MSR Identity Toolbox v1.0: A MATLAB Toolbox for Speaker Recognition Research

    Seyed Omid Sadjadi;Malcolm Slaney;Larry Heck

  • System and method for selecting advertisements

    Malcolm Slaney;Bonnie M. Johnson;Annarosa Tomasi;Steven E. Saunders

  • Canonical correlation analysis of image/control-point location coupling for the automatic location of control points

    Michele Covell;Malcolm Slaney

  • Collaborative filtering and the missing at random assumption

    Benjamin M. Marlin;Richard S. Zemel;Sam Roweis;Malcolm Slaney

  • System and method for automatic classification of speech based upon affective content

    Malcolm Slaney

  • System and method for generating a mood gradient

    William White;Malcolm Slaney

  • Baby ears: a recognition system for affective vocalizations

    Malcolm Slaney;Gerald McRoberts

  • A perceptual pitch detector

    M. Slaney;R.F. Lyon

  • System and method for displaying advertisements

    J. Thomas Ngo;Malcolm Slaney

  • Content-Based Music Information Retrieval: Current Directions and Future Challenges Current retrieval systems can handle tens-of-thousands of music tracks but new systems need to aim at huge online music collections that contain tens-of-millions of tracks.

    Michael A. Casey;Remco Veltkamp;Masataka Goto;Marc Leman

Frequent Co-Authors

Michele Covell
Michele Covell Google (United States)
Kilian Q. Weinberger
Kilian Q. Weinberger Cornell University
Avinash C. Kak
Avinash C. Kak Purdue University West Lafayette
Alain de Cheveigné
Alain de Cheveigné École Normale Supérieure
Richard F. Lyon
Richard F. Lyon Google (United States)
Shihab A. Shamma
Shihab A. Shamma University of Maryland, College Park
Rainer Lienhart
Rainer Lienhart University of Augsburg
Edmund C. Lalor
Edmund C. Lalor University of Rochester
Nima Mesgarani
Nima Mesgarani Columbia University

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