Acoustic Source Separation for Static and Moving Targets
Creators: name: Kornycky, Josef Roger
Publisher: name: University of Surrey
Year: 2010
Subjects: source separation
moving sources
microphone array processing
Independent Component Analysis
adaptive beamforming
Active Intensity Vectors
performance measures

description: The human ability to separate acoustic sources has been termed the cocktail party effect.
description: Digital technology does not share the same inherent ability. The topic of source separation
description: was created to cover techniques that enable electronic separation of acoustical sources.
description: Source separation techniques can be blind in the sense that no additional information is
description: passed to the separation system regarding room geometry, location of the sources, and
description: number of sources. Techniques that assume knowledge of one or more of the previous
description: factors are termed semi-blind.<br>
description: <br>
description: A multitude of solutions for source separation exist but the situation in which the sources
description: are moving is still underpopulated. A moving source causes a change in the mixing conditions and the demixing system must be constantly and speedily updated if output quality
description: is to be maitained. This makes separation of moving sources an even more challenging
description: topic.<br>
description: <br>
description: The applications of source separation research are not simply restricted to the acoustical
description: domain and techniques in this area have previously attributed to advancements in sonar,
description: radar, medical imaging and financial trend detection.<br>
description: <br>
description: The purpose of this project was to further the field of acoustic source separation, investigating both static and moving sources. Firstly, robustness of a number of existing techniques has been analysed. This included statistical techniques, array/beamforming based techniques, and time-frequency sparsity based techniques. This yielded a modification to the
description: more recently proposed Active Intensity Vector (AIV) separation algorithm, which the
description: author named cardioid correction.<br>
description: <br>
description: Secondly, an investigation into how Independent Component Analysis (ICA) and AIV
description: beamforming could be combined was carried out. This resulted in a method for solving the
description: ICA permutation problem (using AIVs) and also two low complexity post-filters. These
description: post-filters exploit information from both ICA and AIV beamforming. The result is a
description: hybrid ICA-AIV separation system.<br>
description: <br>
description: Thirdly, the relationship between existing source separation performance measures and
description: subjective listening test results was studied. A source separation system was modelled
description: to allow controlled variation of the system’s performance. Extensive listening tests were
description: performed and the relationships with the existing performance measures were presented.
description: This enables other researchers to convert their source separation system’s performance into
description: a subjective Mean Opinion Score (MOS).<br>
description: <br>
description: Fourthly, a moving source data set was created and the aforementioned ICA-AIV system
description: and cardioid correction algorithms were combined into a single system. Previously, both
description: these techniques required knowledge of the source locations (azimuth angle with respect
description: to the array). An AIV based target tracking system was proposed which also incorporates
description: a speaker classification system. The classification system can train while the sources are
description: moving (provided there are no large jumps in position for the first 2.5 seconds). Once
description: trained, the system can track changes in source position even if the sources switch position. The system trains on the separation system’s output and hence the entire system is now operating as a Blind Source Separation (BSS) system.<br>