DeLiang Wang is professor of computer science and engineering at The Ohio State University and has become one of the most prominent researchers in the field of speech and hearing technology, making groundbreaking contributions to oscillatory correlation theory and solving speech segregation problem.
Wang’s analysis of neural oscillator networks and his more recent endeavor in segregating the target speech from its acoustic inference are some of his best known known work. Algorithms developed by Wang and his research team on pitch tracking, dereverberation, singing voice separation, mask estimation, and localization-based separation are widely used in the research community. In 2017, IEEE Spectrum ran a cover story on his work towards solving the cocktail party (speech separation) problem.
Wang has published more than 100 scholarly articles in leading journals. He is an Institute of Electrical and Electronics Engineers (IEEE) fellow and presented the 2008 Helmholtz Award from the International Neural Network Society. In 2014 Wang received the university’s Distinguished Scholar Award.