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dc.contributor.advisorDavies, Mike
dc.contributor.authorDmour, Mohammad A.
dc.date.accessioned2011-01-26T10:24:49Z
dc.date.available2011-01-26T10:24:49Z
dc.date.issued2010
dc.identifier.urihttp://hdl.handle.net/1842/4685
dc.description.abstractIn many audio applications, the signal of interest is corrupted by acoustic background noise, interference, and reverberation. The presence of these contaminations can significantly degrade the quality and intelligibility of the audio signal. This makes it important to develop signal processing methods that can separate the competing sources and extract a source of interest. The estimated signals may then be either directly listened to, transmitted, or further processed, giving rise to a wide range of applications such as hearing aids, noise-cancelling headphones, human-computer interaction, surveillance, and hands-free telephony. Many of the existing approaches to speech separation/extraction relied on beamforming techniques. These techniques approach the problem from a spatial point of view; a microphone array is used to form a spatial filter which can extract a signal from a specific direction and reduce the contamination of signals from other directions. However, when there are fewer microphones than sources (the underdetermined case), perfect attenuation of all interferers becomes impossible and only partial interference attenuation is possible. In this thesis, we present a framework which extends the use of beamforming techniques to underdetermined speech mixtures. We describe frequency domain non-linear mixture of beamformers that can extract a speech source from a known direction. Our approach models the data in each frequency bin via Gaussian mixture distributions, which can be learned using the expectation maximization algorithm. The model learning is performed using the observed mixture signals only, and no prior training is required. The signal estimator comprises of a set of minimum mean square error (MMSE), minimum variance distortionless response (MVDR), or minimum power distortionless response (MPDR) beamformers. In order to estimate the signal, all beamformers are concurrently applied to the observed signal, and the weighted sum of the beamformers’ outputs is used as the signal estimator, where the weights are the estimated posterior probabilities of the Gaussian mixture states. These weights are specific to each timefrequency point. The resulting non-linear beamformers do not need to know or estimate the number of sources, and can be applied to microphone arrays with two or more microphones with arbitrary array configuration. We test and evaluate the described methods on underdetermined speech mixtures. Experimental results for the non-linear beamformers in underdetermined mixtures with room reverberation confirm their capability to successfully extract speech sources.en
dc.contributor.sponsorWolfson Microelectronics Scholarshipen
dc.contributor.sponsorInstitute for Digital Communications, University of Edinburgh.en
dc.language.isoenen
dc.publisherThe University of Edinburghen
dc.relation.hasversionM.A. Dmour, M.E. Davies; “A new framework for underdetermined speech extraction using mixture of beamformers”, to appear in IEEE Transactions on Audio, Speech and Language Processing.en
dc.relation.hasversionM.A. Dmour, M.E. Davies; “An approach to under-determined speech separation based on a non-linear mixture of beamformers”, European Conference on Signal Processing (EUSIPCO), 2009.en
dc.relation.hasversionM.A. Dmour, M.E. Davies; “Under-determined speech separation using GMM-based non-linear beamforming”, European Conference on Signal Processing (EUSIPCO), 2008.en
dc.relation.hasversionM.E. Davies, M.A. Dmour; “A nonlinear frequency-domain beamformer for underdetermined speech mixtures”, invited talk at Acoustics’08. The Journal of the Acoustical Society of America, vol. 123, issue 5, p. 3586, 2008.en
dc.subjectaudio source separationen
dc.subjectbeamformingen
dc.subjectunderdetermineden
dc.titleMixture of beamformers for speech separation and extractionen
dc.typeThesis or Dissertationen
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhD Doctor of Philosophyen


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