This paper describes the algorithm and convergence properties of an adaptive echo canceller with linear predictor. Conventional echo cancellers based on the learning identification algorithm may not provide good performance, because the rate of convergence is low due to the high correlation of speech signals, and echoes at the beginning of calls cannot be cancelled. In order to obtain better convergence properties, the new echo canceller adopts a linear prediction as the method for decorrelating the speech signals. The identification of the echo path and the generation of the echo replica are conducted independently, and the identification of echo path is carried out with prediction errors of speech signals and echo signal when predictor coefficients are decided by the linear prediction of speech signals. The echo replica is generated by putting the received speech signal through the echo path model. Computer simulation has shown that the new echo canceller is converged faster than conventional echo cancellers and that the convergence properties are better as the degree of linear prediction is higher and the predictor coefficients are more accurate. In case the degree is five, the rate of convergence is about twice as high and Echo Return Loss Enhancement (ERLE) increases over 10 dB in comparison with the conventional one.

As a means of improving the rate of convergence of the conventional echo canceller using the learning identification method, the authors have previously proposed a linear predictive algorithm. This algorithm shows better convergence than the learning identification method. However, in this algorithm, as well as in the learning identification method, a compromise is necessary between a relatively large step gain required for fast convergence and the relatively small step gain needed for noise insensitivity in the presence of noise. In this paper a new algorithm based on the linear predicitive algorithm is proposed, in which the step gain is determined as a function of the estimated values of noise and the parameters-error of the echo path model in order to improve both the rate of convergence and the noise insensitivity simultaneously. The efficiency of the proposed algorithm is examined by computer simulations. It has been shown that the proposed algorithm gives about twice the rate of convergence and about 10 dB lower parameters-error in the stationary state in comparison with the learning identification method. Besides, it has been proved that this algorithm guarantees non-divergence of the echo path model even during the period of double-talking" without any control device such as a double-talking detector.