Fast and versatile blind separation of diverse sounds using closed-form estimation of probability density functions of sources

In this paper, we propose a fast and versatile blind source separation including closed-form estimation of sources´ probability density functions (PDFs), where the ICA´s activation function is automatically adapted to various noise conditions. In the proposed method, closed-form second-order ICA and closed-form PDF estimation are introduced as a computational-cost-efficient preprocessing to extract sources´ PDFs. Compared with various type of conventional ICAs, e.g., fixed activation-function type and ML-based type, our proposed algorithm can give a faster and higher convergence. Experimental assessment reveals that the proposed method is versatile for handling non-speech sound sources.