Enhancing Blind Source Separation in the Cocktail Party Problem Using Independent Component Analysis

Independent Component Analysis makes services as diverse as possible by reducing the second-order system and any higher-order dependence on the data provided. It is based on the assumptions of statistically independent substrates from many unknown sources. The ICA is based on the black box method which means it does not need to know internal features. Therefore, in this paper, the basic principles of how to Divide the Blind Source (Mode of Unchecked Reading) will be used to solve a real-world problem such as the problem where a cocktail party is going on. In addition, a pointer-based approach is illustrated to explain the ICA’s previous processing steps in terms of the audio issues caused while recording in a part of Cocktail party and the non-mix processes in the ICA. In addition, a variety of advanced methods of evaluating independent sources in the ICA model will be compared.