Abstract
Human brain has the ability to focus on desired acoustic source when several sources are active. In the domain of digital electronics this problem is termed as the cock- tail party problem. Over the past few decades many algorithms have been proposed which attempt to solve this problem; they are generally termed as acoustic source separation algorithms. The proposed algorithms achieve separation of individual source components from observed acoustic mixtures. The source separation system may be capable of estimating the number of sources, their physical locations, the room impulse response and/or any target source signal information. A system that approximates this information is termed as blind. Source separation systems which require any such information beforehand are termed as semi-blind. Most of the proposed source separation algorithms deal with acoustic sources that are stationary in space. A more challenging task is to approximate unmixing filters while the sources are constantly moving. To maintain output performance in such a scenario, the source separation system has to swiftly and accurately detect the time variant mix- ing parameters, and update unmixing filters accordingly. The area of moving sources has still not been heavily investigated by researchers. The aim of this thesis is to further the field of acoustic source separation. Investigation of intensity vector direction (IVD) based source separation algorithm was carried out to analyse and improve the system, both in terms of applicability and output sound quality. The algorithm under investigation provides a robust and nearly closed-form solution to the source separation problem with a low processing time. However, the algorithm initially required unmixing filter coefficients as input for dealing with practical acoustic scenarios. Analysis performed with microphone array response, microphone array geometry and the room response yielded three different modifications to the baseline system, improving system applicability and output sound quality. The IVD based system was investigated to deal with more challenging acoustic scenarios, such as time variant number of sources. Likewise, the IVD statistics were analysed to propose solutions for moving sources scenario. The system exhibited potential to swiftly, accurately and reliably detect changes in the time varying mixing parameters. As a result of these investigations, a novel system pipeline is proposed, capable of detecting, tracking and separating moving sources in a blind manner. The proposed algorithms were evaluated for processing time and separation performance. Optimisation of output sound quality was carried out through objective performance measures, while speaker tracking was evaluated subjectively. Finally, a demonstration was developed in Matlab based on the proposed algorithms to facilitate user interaction with the surrounding acoustic environment.