We examine the problem of blind audio source separation using Independent Component Analysis (ICA). In order to separate audio sources recorded in a real recording environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require working in the frequency domain. This paper proposes a fixed-point algorithm for performing frequency domain ICA on speech and audio sources, aiming for faster convergence. The algorithm was tested on a series of speech and audio samples with quite promising results.
Moreover, we are examining a method to increase the stability and enhance the performance of previous maximum likelihood frequency domain ICA algorithms.
