M.S. Thesis Defense: Yang Jiao
Tuesday, December 3, 2019
301 405 3681
Recently, deep neural networks have achieved incredible success in the area of computer vision and natural language processing. The research topics under the umbrella of speech enhancement have embraced this chance to revolutionize. Dereverberation as one such topics has gained less popularity compared to other tasks of speech enhancement such as cocktail party problem, speech enhancement in open area. Our aim is to extend method of deep learning into the domain of dereverberation.
We leverage a successful neural network method on a similar task of speech enhancement to dereverberation, specifically, time frequency mask supported GEV beamformer. This data driven approach introduces feature transferrable to related tasks compared to hand-engineered methods. Our experiments illustrate that the original framework arise from open area enhancement tasks is proved to be effective in our closure space tasks.