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Title: Blind signal separation
Author: Chan, D. C. B.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 1997
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The separation of independent sources from mixed observed data is a fundamental and challenging signal processing problem. In many practical situations, one or more desired signals need to be recovered from the mixtures only. A typical example is speech recordings made in an acoustic environment in the presence of background noise and/or competing speakers. Other examples include EEG signals, passive sonar applications and cross-talk in data communications. The audio signal separation problem is sometimes referred to as The Cocktail Party Problem. When several people in the same room are conversing at the same time, it is remarkable that a person is able to choose to concentrate on one of the speakers and listen to his or her speech flow unimpeded. This ability, usually referred to as the binaural cocktail party effect, results in part from binaural (two-eared) hearing. In contrast, a person with a severe hearing loss in one ear finds it is difficult to focus on a particular speaker under the same circumstances. A signal separation pre-process would be desirable in such circumstances. Signal separation techniques can also be applied in many other areas such as noise reduction, speech recognition and multi-media applications. The term 'Blind Signal Separation' refers to the lack of any propagation model: only statistical independence of the sources is assumed. The lack of other prior information underlines the difficulty of the problem. Observations may be modelled as linear mixtures of a number of source signals, i.e. a linear multi-input multi-output system. In this dissertation, the general n-source n-sensor (n x n) linear time invariant wide-band system is studied, in which, n random signals are received at n sensors and these signals originated from n sources. The problem is to recover the sources from observed signals only. Various block-based iterative algorithms are proposed which use output decorrelation as a signal separation criterion. These algorithms search for a linear transformation that minimises the statistical correlation between the components. Some existing solutions are reviewed and compared.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available