Title:
Some new applications of phase information to speech processing

dc.contributor.advisor Lee, Chin-Hui
dc.contributor.author Li, Kehuang
dc.contributor.committeeMember Juang, Biing Hwang
dc.contributor.committeeMember Clements, Mark
dc.contributor.committeeMember Li, Geoffery
dc.contributor.committeeMember Xie, Yao
dc.contributor.department Electrical and Computer Engineering
dc.date.accessioned 2020-05-20T16:47:28Z
dc.date.available 2020-05-20T16:47:28Z
dc.date.created 2019-05
dc.date.issued 2019-01-15
dc.date.submitted May 2019
dc.date.updated 2020-05-20T16:47:28Z
dc.description.abstract With the fast growing of deep neural network models, more and more tasks have been boosted when move on to deep models. Speech processing applications, e.g., speech enhancement, speech bandwidth expansion, dereverberataion, and etc., are also benefited. Most deep models focus more on improving the estimation of the spectral magnitude. However, there are evidences showing that the phase spectra are as well informative. Therefore, this dissertation investigates practical approaches to recover the spectral phase by resolving two inconsistency issues, i.e., frame-length inconsistency and frame-overlap inconsistency, leveraging the success of convex programming and alternating projection, respectively. Furthermore, frameworks to integrate both of the methods are explored. The proposed approaches and frameworks, taking advantage of some speech signal characteristics, have very limited number of assumptions, and therefore can be applied to various speech processing tasks.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/62636
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Speech processing
dc.subject Phase
dc.subject Deep neural network
dc.title Some new applications of phase information to speech processing
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Lee, Chin-Hui
local.contributor.corporatename School of Electrical and Computer Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication b35c0c49-bee2-49ad-9d6a-867b4ba8908b
relation.isOrgUnitOfPublication 5b7adef2-447c-4270-b9fc-846bd76f80f2
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
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