World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
47
Citations
9691
World Ranking
6450
National Ranking
53

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)
Tei-Wei Kuo
Tei-Wei Kuo National Taiwan University
Lin-Shan Lee
Lin-Shan Lee National Taiwan University

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