Modeling audio directional statistics using a complex bingham mixture model for blind source extraction from diffuse noise

Mask estimation is a central task in blind signal processing including source separation, denoising, and multi-source localization. In this paper, we define a complex Bingham mixture model (cBMM), and propose it as a model of directional statistics for mask estimation. The complex Bingham distribution can represent not only rotationally symmetric but also rotationally asymmetric distributions. Therefore, it can precisely model stochastic variation of the directional statistics due to reverberation, noise, source movement, etc., which is not necessarily rotationally symmetric. In an experimental evaluation, the proposed cBMM outperformed a conventional complex Watson mixture model (cWMM) in terms of blind source extraction from diffuse noise, reducing the word error rate by 0.91% absolute on CHiME-3 challenge data.