This thesis concerns blind source separation techniques using second order statistics and higher order statistics for reverberant environments. A focus of the thesis is algorithmic simplicity with a view to the algorithms being implemented in their online forms. The main challenge of blind source separation applications is to handle reverberant acoustic environments; a further complication is changes in the acoustic environment such as when human speakers physically move. A novel time-domain method which utilises a pair of finite impulse response filters is proposed. The method of principle angles is defined which exploits a singular value decomposition for their design. The pair of filters are implemented within a generalised sidelobe canceller structure, thus the method can be considered as a beamforming method which cancels one source. An adaptive filtering stage is then employed to recover the remaining source, by exploiting the output of the beamforming stage as a noise reference. A common approach to blind source separation is to use methods that use higher order statistics such as independent component analysis. When dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in independent component analysis, across the frequency bins. Independent vector analysis directly addresses this issue by modeling the dependencies between frequency bins, namely making use of a source vector prior. An alternative source prior for real-time (online) natural gradient independent vector analysis is proposed. A Student's t probability density function is known to be more suited for speech sources, due to its heavier tails, and is incorporated into a real-time version of natural gradient independent vector analysis. The final algorithm is realised as a real-time embedded application on a floating point Texas Instruments digital signal processor platform. Moving sources, along with reverberant environments, cause significant problems in realistic source separation systems as mixing filters become time variant. A method which employs the pair of cancellation filters, is proposed to cancel one source coupled with an online natural gradient independent vector analysis technique to improve average separation performance in the context of step-wise moving sources. This addresses `dips' in performance when sources move. Results show the average convergence time of the performance parameters is improved. Online methods introduced in thesis are tested using impulse responses measured in reverberant environments, demonstrating their robustness and are shown to perform better than established methods in a variety of situations.

<p>This Master’s Thesis was part of the project called Multimodalanalysis at the Depart-ment of Biomedical Engineering and Informatics at the Ume˚ University Hospital inUme˚ Sweden. The aim of the project is to develop multivariate measurement anda,analysis methods of the skeletal muscle physiology. One of the methods used to scanthe muscle is functional ultrasound. In a study performed by the project group datawas aquired, where test subjects were instructed to follow a certain exercise scheme,which was measured. Since there currently is no superior method to analyze the result-ing data (in form of ultrasound video sequences) several methods are being looked at.One considered method is called Independent Vector Analysis (IVA). IVA is a statisticalmethod to find independent components in a mix of components. This Master’s Thesisis about segmenting and analyzing the ultrasound images with help of IVA, to validateif it is a suitable method for this kind of tasks.First the algorithm was tested on generated mixed data to find out how well itperformed. The results were very accurate, considering that the method only usesapproximations. Some expected variation from the true value occured though.When the algorithm was considered performing to satisfactory, it was tested on thedata gathered by the study and the result can very well reflect an approximation of truesolution, since the resulting segmented signals seem to move in a possible way. But themethod has weak sides (which have been tried to be minimized) and all error analysishas been done by human eye, which definitly is a week point. But for the time being itis more important to analyze trends in the signals, rather than analyze exact numbers.So as long as the signals behave in a realistic way the result can not be said to becompletley wrong. So the overall results of the method were deemed adequate for the application at hand.</p> / Multimodalanalys

This Master’s Thesis was part of the project called Multimodalanalysis at the Depart-ment of Biomedical Engineering and Informatics at the Ume˚ University Hospital inUme˚ Sweden. The aim of the project is to develop multivariate measurement anda,analysis methods of the skeletal muscle physiology. One of the methods used to scanthe muscle is functional ultrasound. In a study performed by the project group datawas aquired, where test subjects were instructed to follow a certain exercise scheme,which was measured. Since there currently is no superior method to analyze the result-ing data (in form of ultrasound video sequences) several methods are being looked at.One considered method is called Independent Vector Analysis (IVA). IVA is a statisticalmethod to find independent components in a mix of components. This Master’s Thesisis about segmenting and analyzing the ultrasound images with help of IVA, to validateif it is a suitable method for this kind of tasks.First the algorithm was tested on generated mixed data to find out how well itperformed. The results were very accurate, considering that the method only usesapproximations. Some expected variation from the true value occured though.When the algorithm was considered performing to satisfactory, it was tested on thedata gathered by the study and the result can very well reflect an approximation of truesolution, since the resulting segmented signals seem to move in a possible way. But themethod has weak sides (which have been tried to be minimized) and all error analysishas been done by human eye, which definitly is a week point. But for the time being itis more important to analyze trends in the signals, rather than analyze exact numbers.So as long as the signals behave in a realistic way the result can not be said to becompletley wrong. So the overall results of the method were deemed adequate for the application at hand. / Multimodalanalys