A synchronized learning algorithm for reflection coefficients and tap weights in a joint lattice predictor and transversal filter

In order to achieve fast convergence and less computation for adaptive filters, a joint method combining a whitening process and the NLMS algorithm is a hopeful approach. One way is to combine a lattice predictor and a transversal filter supervised by the NLMS algorithm. However, the filter coefficient adaptation is very sensitive to the reflection coefficient fluctuation. The reason for this instability is analyzed. The filter coefficients are updated one sample behind the reflection coefficient update. This causes large error, in other words, sensitivity of their mismatch is very high on filter characteristics. An improved learning method is proposed in order to compensate for this mismatch. The convergence property is close to that of the RLS algorithm. Computational complexity can be well reduced from that of the RLS algorithm. Simulation results using real voices demonstrate the usefulness of the proposed method.