Acoustic echo cancellation using NLMS-neural network structures

One of the limitations of linear adaptive echo cancellers is nonlinearities which are generated mainly in the loudspeaker. The complete acoustic channel can be modelled as a nonlinear system convolved with a linear dispersive echo channel. Two new acoustic echo canceller models are developed to improve nonlinear performance. The first model consists of a time-delay feedforward neural network (TDNN) and the second model consists of a memoryless neural network followed by an adaptive Normalized Least Mean Square (NLMS) structure. Simulations demonstrate that both neural network based structures improve the Echo Return Loss Enhancement (ERLE) per