This study presents a novel blind source extraction of a noisy mixture using a class of parallel linear predictor filters. Analysis of a noisy mixture equation is carried out to address new autoregressive source signal model based on the covariance matrix of the whitened data. A method of interchanging the rules of filter inputs is proposed such that this matrix becomes the filter input while the estimated source signals are considered as the parallel filter coefficients. As the matrix has unity norm and unity eigenvalues, the filter becomes independent on the mixture signal norm and eigenvalues variations, thus solving drastically the ambiguity due to the dependency of the filter on the mixture power levels if the mixture is considered as the filter input. Furthermore, the unity eigenvalues of the matrix result in a very fast convergence in two iterations. Simulation results show that the model is capable of extracting the unknown source signals and removing noise when the input signal-to-noise ratio is varied from −20 to 80 dB.