The modeling of the acoustic echo path was presented using multiple of small
adaptive filters rather than using one long adaptive filter. A new approach was
proposed using the concept of decomposing the long adaptive filter into low order
multiple sub-filters in which the error signals are independent on each other. The
independency of the error signals exhibit the parallelism technique. In this way
we achieved our goal in increasing the speed of the convergence rate. The
proposed algorithm was also compared with multiple sub-filters approach used
for acoustic echo cancellation as the technique of decomposition of error. This
technique is based on using multiple sub-adaptive filters in which the error
signals are dependent on each other. In this way the parallelism technique is not
achieved and as the result the convergence rate increases. This thesis addresses
also the problems of blind source separation (BSS). In blind source separation,
signals from multiple sources arrive simultaneously at a sensor array, so that
each sensor output contains a mixture of source signals. Sets of sensor outputs are
processed to recover the source signals from the mixed observations.