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dc.contributor.advisorHopgood, James R.
dc.contributor.advisorBell, Judith
dc.contributor.authorEvers, Christine
dc.date.accessioned2011-02-02T11:35:36Z
dc.date.available2011-02-02T11:35:36Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/1842/4761
dc.description.abstractSpeech signals radiated in confined spaces are subject to reverberation due to reflections of surrounding walls and obstacles. Reverberation leads to severe degradation of speech intelligibility and can be prohibitive for applications where speech is digitally recorded, such as audio conferencing or hearing aids. Dereverberation of speech is therefore an important field in speech enhancement. Driven by consumer demand, blind speech dereverberation has become a popular field in the research community and has led to many interesting approaches in the literature. However, most existing methods are dictated by their underlying models and hence suffer from assumptions that constrain the approaches to specific subproblems of blind speech dereverberation. For example, many approaches limit the dereverberation to voiced speech sounds, leading to poor results for unvoiced speech. Few approaches tackle single-sensor blind speech dereverberation, and only a very limited subset allows for dereverberation of speech from moving speakers. Therefore, the aim of this dissertation is the development of a flexible and extendible framework for blind speech dereverberation accommodating different speech sound types, single- or multiple sensor as well as stationary and moving speakers. Bayesian methods benefit from – rather than being dictated by – appropriate model choices. Therefore, the problem of blind speech dereverberation is considered from a Bayesian perspective in this thesis. A generic sequential Monte Carlo approach accommodating a multitude of models for the speech production mechanism and room transfer function is consequently derived. In this approach both the anechoic source signal and reverberant channel are estimated using their optimal estimators by means of Rao-Blackwellisation of the state-space of unknown variables. The remaining model parameters are estimated using sequential importance resampling. The proposed approach is implemented for two different speech production models for stationary speakers, demonstrating substantial reduction in reverberation for both unvoiced and voiced speech sounds. Furthermore, the channel model is extended to facilitate blind dereverberation of speech from moving speakers. Due to the structure of measurement model, single- as well as multi-microphone processing is facilitated, accommodating physically constrained scenarios where only a single sensor can be used as well as allowing for the exploitation of spatial diversity in scenarios where the physical size of microphone arrays is of no concern. This dissertation is concluded with a survey of possible directions for future research, including the use of switching Markov source models, joint target tracking and enhancement, as well as an extension to subband processing for improved computational efficiency.en
dc.contributor.sponsorScottish Funding Councilen
dc.contributor.sponsorJoint Research Institute in Signal & Image Processing (JRI-SIP) at the University of Edinburgh as part of the Edinburgh Research Partnership in Engineeringen
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionC. Evers and J. R. Hopgood, “Parametric models for single-channel blind dereverberation of speech from a moving speaker,” IET J. Signal Process., vol. 2, no. 2, pp. 59–74, Jun. 2008.en
dc.relation.hasversionC. Evers and J. R. Hopgood, “Multichannel online blind speech dereverberation with marginalization of static observation parameters in a Rao-Blackwellized particle filter,” Springer J. Signal Process. Systems, 2009.en
dc.relation.hasversionC. Evers, J. R. Hopgood, and J. Bell, “Acoustic models for online blind source dereverberation using sequential monte carlo methods,” in Proc. IEEE Conf. ICASSP, Las Vegas, NV, 24 Mar. - 4 Apr. 2008.en
dc.relation.hasversionC. Evers, J. R. Hopgood, and J. Bell, “Blind speech dereverberation using batch and sequential monte carlo methods,” in Proc. IEEE Conf. ISCAS, Seattle, WA, 18-21 May 2008, invited paper.en
dc.relation.hasversionC. Evers and J. R. Hopgood, “Marginalization of static observation parameters in a Rao-Blackwellized particle filter with application to sequential blind speech dereverberation,” in Proc. EUSIPCO, Glasgow, UK, Aug. 2009.en
dc.relation.hasversionJ. R. Hopgood, C. Evers, and S. Fortune, “Bayesian single channel blind dereverberation of speech from a moving speaker,” in Speech dereverberation, P. A. Naylor and N. Gaubitch, Eds. Springer, 2010.en
dc.relation.hasversionJ. R. Hopgood and C. Evers, “Towards single-channel blind dereverberation of speech from a moving speaker,” in IMA Intl. Conf. Math. Sig. Proc., Dec. 2006en
dc.relation.hasversionJ. R. Hopgood and C. Evers, “Block-based TVAR models for single-channel blind dereverberation of speech from a moving speaker,” in Proc. IEEE Conf. SSP, Madison, WI, 2007, pp. 274–277.en
dc.subjectreverberationen
dc.subjectdegradationen
dc.subjectspeech intelligibilityen
dc.subjectblind speech dereverberationen
dc.subjectRao-Blackwellisationen
dc.subjectMonte Carloen
dc.subjectdereverberationen
dc.titleBlind dereverberation of speech from moving and stationary speakers using sequential Monte Carlo methodsen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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