D
DeLiang Wang
Researcher at Ohio State University
Publications - 475
Citations - 28623
DeLiang Wang is an academic researcher from Ohio State University. The author has contributed to research in topics: Speech processing & Speech enhancement. The author has an hindex of 82, co-authored 440 publications receiving 23687 citations. Previous affiliations of DeLiang Wang include Massachusetts Institute of Technology & Tsinghua University.
Papers
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Journal ArticleDOI
On training targets for supervised speech separation
TL;DR: Results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics, and that masking based targets, in general, are significantly better than spectral envelope based targets.
Journal ArticleDOI
Supervised Speech Separation Based on Deep Learning: An Overview
DeLiang Wang,Jitong Chen +1 more
TL;DR: A comprehensive overview of deep learning-based supervised speech separation can be found in this paper, where three main components of supervised separation are discussed: learning machines, training targets, and acoustic features.
Journal ArticleDOI
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
DeLiang Wang,Guy J. Brown +1 more
TL;DR: This paper focuses on the development of model-Based Speech Segregation in CASA systems, which was first introduced in 2000 and has since been upgraded to a full-blown model-based system.
Journal ArticleDOI
Complex ratio masking for monaural speech separation
TL;DR: The proposed approach improves over other methods when evaluated with several objective metrics, including the perceptual evaluation of speech quality (PESQ), and a listening test where subjects prefer the proposed approach with at least a 69% rate.
Book ChapterDOI
On Ideal Binary Mask As the Computational Goal of Auditory Scene Analysis
TL;DR: This chapter is an attempt at a computational-theory analysis of auditory scene analysis, where the main task is to understand the character of the CASA problem.