Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/64805
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dc.contributor.authorGeng, Xiuhua
dc.date.accessioned2015-06-04T06:05:31Z
dc.date.available2015-06-04T06:05:31Z
dc.date.copyright2014en_US
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/10356/64805
dc.description.abstractSpeech enhancement aims to improve the performance of speech processing systems operating in various noisy environments. The performance of speech enhancement algorithm can be evaluated by two uncorrelated criteria: clarity and intelligibility. Here, we present a speech enhancement algorithm based on the signal subspace method, which can be adopted for arbitrary noise types. Firstly, an equalizer is introduced to whiten the noise embedded in the noisy speech signal. Then, by applying the Karhunen-Loeve transform (KLT), the noisy speech signal is decomposed into two subspaces: noise subspace and signal-plus-noise subspace. The clean signal can be estimated from the signal-plus-noise subspace after eliminating the noise subspace. By assuming the noise is additive and uncorrelated with clean signal, the recovery of the original signal is conducted frame-by-frame by introducing two criteria: Time Domain Constrained (TDC) and Spectral Domain Constrained (SDC). TDC is used to alleviate the signal distortion when the energy of the residual noise is below a certain threshold. SDC can be utilized to minimize the signal distortion under a fixed spectrum of the residual noise. Simulation results show that our proposed algorithm is able to deal with the arbitrary noise types effectively. Index Terms- Karhunen-Loeve transform, TDC, SDC, signal subspace, speech enhancement.en_US
dc.format.extent67 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.titleA signal subspace approach for speech enhancementen_US
dc.typeThesis
dc.contributor.supervisorSoon Ing Yannen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
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