The world today is relying on hands free devices and teleconferencing equipments but the major problem with the use of these systems is creation of acoustic echo at the time of conversation. The quality of information to be transmitted and received is degraded due to the echo. Hence, echo cancellation has come up as the most interesting as well as challenging part in any communication system. Echo is the degraded or delayed version of an original signal which comes back to the source after successive reflections from the surroundings. The removal of echo without degrading the quality of an original signal is a challenge of research in present time. Echo cancellation in case of voice communication is removal of echo, improving the quality of an original voice signals. In our thesis we have focused on the acoustic echo cancellation in closed room with the help of adaptive filters. Stereo echo cancellation is a key technique for solving the echo problem in modern teleconferencing systems. There is a fundamental problem in stereo echo cancellation, the misalignment between the converged filter weights and the real room impulse responses. Most existing works explain the misalignment problem by a conceptual stochastic equation, and no studies have investigated the closed form of the final weights in a stereo echo canceller. In our thesis we have proposed a subband adaptive modeling approach to develop an effective system of stereo echo cancellation with the help of better adaptive filtering techniques. We have adopted the subband adaptive modeling because the fullband adaptive modeling technique may not be able to characterize the electro-acoustic performance of a Multichannel Compression Hearing Aid (MCHA). We also employ the design of DFT filter banks for the analysis and synthesis of the signal and in the place of conventional NLMS or RLS algorithm; we have rather employed the APA (Affine Projection Algorithm) for adaptive modeling in each subband. The performance of the subband adaptive modeling is simulated under various conditions in MATLAB and the results are analyzed. The performance of the subband adaptive modeling system is better and effective as compared to the fullband adaptive modeling system. The results for various input and noise signals are plotted and shown in result section of this report.