Audio source separation using independent component analysis and beam formation

Project Report from the year 2013 in the subject Audio Engineering grade: 10 course: ECE language: English abstract: Audio source separation is the problem of automated separation of audio sources present in a room using a set of differently placed microphones capturing the auditory scene. The whole problem resembles the task a human can solve in a cocktail party situation where using two sensors (ears) the brain can focus on a specific source of interest suppressing all other sources present (cocktail party problem).For computational and conceptual simplicity this problem is often represented as a linear transformation of the original audio signals. In other words each component (multivariate signal) of the representation is a linear combination of the original variables (original subcomponents). In signal processing independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents by assuming that the subcomponents are non-Gaussian signals and that they are all statistically independent from each other. Such a representation seems to capture the essential structure of the data in many applications.Here we separate audio using different criteria suggested for ICA being PCA (Principal Component Analysis) Non-gaussianity maximization using kurtosis and neg-entropy methods frequency domain approach using non-gaussianity maximization and beamforming.