Blind separation for convolutive mixtures of non-stationary signals
Résumé
Abstract-This paper proposes a method of ''blind separation'' which extracts non-stationary signals (e.g., speech signals, music) from their convolutive mixtures. The function is acquired by modifying a network's parameters so that a cost function takes the minimum at any time. the cost function is the one introduced by Matsuoka et al. The learning rule is derived from the natural gradient minimization of the cost function. The validity of the proposed method is confirmed by computer simulation.