World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
9691
World Ranking
6451
National Ranking
53

Yu Tsao 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 Yu Tsao 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: 308 publications — 75th percentile

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

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

Yu Tsao 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 Yu Tsao 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: 47 D-Index — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Overview

Yu Tsao is affiliated with the Research Center for Information Technology Innovation at Academia Sinica in Taiwan. Their research primarily spans the field of Computer Science, with a focus on subfields such as Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Cognitive Neuroscience, and Electrical and Electronic Engineering.

The major topics explored in Yu Tsao's work include:

  • Speech and Audio Processing
  • Speech Recognition and Synthesis
  • Music and Audio Processing
  • Hearing Loss and Rehabilitation
  • Advanced Adaptive Filtering Techniques
  • Indoor and Outdoor Localization Technologies
  • Voice and Speech Disorders

Yu Tsao has contributed extensively to several publication venues. The most frequent among these are:

  • arXiv (Cornell University)
  • Interspeech 2022
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • Scientific Reports

Among the recent papers authored or co-authored by Yu Tsao are the following:

  • "ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech" (2020), published in Computer Speech & Language
  • "Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning" (2020), published in Scientific Reports
  • "Conditional Diffusion Probabilistic Model for Speech Enhancement" (2022), presented at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Forecasting Air Quality in Taiwan by Using Machine Learning" (2020), published in Scientific Reports
  • "Deep Learning-Based Non-Intrusive Multi-Objective Speech Assessment Model With Cross-Domain Features" (2022), published in IEEE/ACM Transactions on Audio Speech and Language Processing

Frequent collaborators in Yu Tsao's research include:

  • Hsin-Min Wang
  • Szu-Wei Fu
  • Ryandhimas E. Zezario
  • Xugang Lu
  • Kai-Chun Liu

Best Publications

  • Speech enhancement based on deep denoising autoencoder.

    Xugang Lu;Yu Tsao;Shigeki Matsuda;Chiori Hori

  • ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

    Xin Wang;Junichi Yamagishi;Junichi Yamagishi;Massimiliano Todisco;Héctor Delgado

  • Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks

    Chin-Cheng Hsu;Hsin-Te Hwang;Yi-Chiao Wu;Yu Tsao

  • Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders

    Hsin-Tien Chiang;Yi-Yen Hsieh;Szu-Wei Fu;Kuo-Hsuan Hung

  • End-to-End Waveform Utterance Enhancement for Direct Evaluation Metrics Optimization by Fully Convolutional Neural Networks

    Szu-Wei Fu;Tao-Wei Wang;Yu Tsao;Xugang Lu

  • Voice conversion from non-parallel corpora using variational auto-encoder

    Chin-Cheng Hsu;Hsin-Te Hwang;Yi-Chiao Wu;Yu Tsao

  • Detection of Pathological Voice Using Cepstrum Vectors: A Deep Learning Approach.

    Shih Hau Fang;Yu Tsao;Min Jing Hsiao;Ji Ying Chen

  • Audio-Visual Speech Enhancement Using Multimodal Deep Convolutional Neural Networks

    Jen-Cheng Hou;Syu-Siang Wang;Ying-Hui Lai;Yu Tsao

  • Raw waveform-based speech enhancement by fully convolutional networks

    Szu-Wei Fu;Yu Tsao;Xugang Lu;Hisashi Kawai

  • MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement.

    Szu-Wei Fu;Chien-Feng Liao;Yu Tsao;Shou-De Lin

  • S1 and S2 Heart Sound Recognition Using Deep Neural Networks

    Tien-En Chen;Shih-I Yang;Li-Ting Ho;Kun-Hsi Tsai

  • A recommendation mechanism for contextualized mobile advertising

    Soe-Tsyr Yuan;Y.W. Tsao

  • SNR-Aware Convolutional Neural Network Modeling for Speech Enhancement.

    Szu-Wei Fu;Yu Tsao;Xugang Lu

  • MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement

    Szu-Wei Fu;Cheng Yu;Tsun-An Hsieh;Peter Plantinga

  • MOSNet: Deep Learning-Based Objective Assessment for Voice Conversion.

    Chen-Chou Lo;Szu-Wei Fu;Wen-Chin Huang;Xin Wang

  • Quality-Net: An End-to-End Non-intrusive Speech Quality Assessment Model Based on BLSTM.

    Szu-Wei Fu;Yu Tsao;Hsin-Te Hwang;Hsin-Min Wang

  • Complex spectrogram enhancement by convolutional neural network with multi-metrics learning

    Szu-Wei Fu;Ting-yao Hu;Yu Tsao;Xugang Lu

  • Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

    Yu-Heng Lai;Wei-Ning Chen;Te-Cheng Hsu;Che Lin

  • A Deep Denoising Autoencoder Approach to Improving the Intelligibility of Vocoded Speech in Cochlear Implant Simulation

    Ying-Hui Lai;Fei Chen;Syu-Siang Wang;Xugang Lu

  • Learning Transportation Modes From Smartphone Sensors Based on Deep Neural Network

    Shih-Hau Fang;Yu-Xaing Fei;Zhezhuang Xu;Yu Tsao

Frequent Co-Authors

Hsin-Min Wang
Hsin-Min Wang Academia Sinica
Chin-Hui Lee
Chin-Hui Lee Georgia Institute of Technology
Tomoki Toda
Tomoki Toda Nagoya University
Hung-yi Lee
Hung-yi Lee National Taiwan University
Shao-Yi Chien
Shao-Yi Chien National Taiwan University
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics
Satoshi Nakamura
Satoshi Nakamura Nara Institute of Science and Technology
Jinyu Li
Jinyu Li Microsoft (United States)
Sabato Marco Siniscalchi
Sabato Marco Siniscalchi Georgia Institute of Technology
Tei-Wei Kuo
Tei-Wei Kuo National Taiwan University

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