Source adaptive blind signal extraction using closed-form ICA for hands-free robot spoken dialogue system

In this paper, we propose a new ICA-based BSS algorithm including estimation of sources´ probability density functions (PDFs) to adapt the nonlinear activation function to various noise conditions. In the proposed method, closed-form second-order ICA is introduced as a computational-cost-efficient preprocessing to extract sources´ PDFs, which is beneficial for real-time application. 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. Based on the proposed source-adaptive ICA, we show a real-time noise reduction results under diffuse noise environment. Also we can demonstrate our recently developed hands-free robot spoken dialogue system via real-time ICA.