Noise adaptive optimization of matrix initialization for frequency-domain independent component analysis

Initializing an unmixing matrix is an important problem in source separation since an objective function to be optimized is typically non-convex. In this paper, we consider the problem of two-source signal separation from a two-microphone array located on a mobile device, where a point source such as a speech signal is placed in front of the array, while no information is available about another interference signal. We propose a simple and computationally efficient method for estimating the geometry and source type (a point or diffuse) of the interference signal, which allows us to adaptively choose a suitable unmixing matrix initialization scheme. Our proposed method, noise adaptive optimization of matrix initialization (NAOMI), is shown to be effective through source separation simulations.