The human auditory system is remarkable in its ability to function in busy acoustic environments. It is able to selectively focus attention on and extract a single source of interest in the midst of competing acoustic sources, reverberation and motion. Yet this problem, which is so elementary for most human listeners has proven to be a very difficult one to solve computationally. Even more difficult has been the search for practical solutions to problems to which digital signal processing can be applied. Many applications that would benefit from a solution such as hearing aid systems, industrial noise control, or audio surveillance require that any such solution be able to operate in real time and consume only a minimal amount of computational resources.
In this thesis, a novel solution to the cocktail party problem is proposed. This solution is rooted in the field of Computational Auditory Scene Analysis, and makes use of insights regarding the processing carried out by the early human auditory system in order to effectively suppress interference. These neurobiological insights have been thus adapted in such a way as to produce a solution to the cocktail party problem that is practical from an engineering point of view. The proposed solution has been found to be robust under a wide range of realistic environmental conditions, including spatially distributed interference, as well as reverberation.