Echo cancellation in voice communication is a process of removing the echo to improve the clarity, quality of the voice signals by suppressing the silence signal which prevents echoes during transmission through networks. There are two types of echo in voice communication: Hybrid echo cancellation and Acoustic echo cancellation. Hybrid echo is generated by the reflection of electrical energy by a device called Hybrid. Echo suppressor’s helps to minimize the hybrid echo’s to produce clarity voice signals. The coupling problems between the telephony speaker and its microphone lead to the acoustic echo’s. The direct sound from the loud speaker enters into the microphone almost unaltered this is called direct acoustic path Echo. These echoes may be caused by cross talks or by echo in caller surroundings. These disturbances vary depending on environmental preliminaries such as ventilators, fans, walls and other disturbing sources. The main objective of this research is to present acoustic echo cancellation design methods. We investigate two parts of echo cancellation design: In the first part we focus on echo cancellation for sinusoidal signal using different algorithms like Least mean square algorithm(LMS),Leaky Least Mean Square (LLMS) Algorithm, Normalized Mean Square (NLMS) Algorithm, and Recursive Least Square(RLS) Algorithm based on different parameters .The second part our work focus on the robustness of Acoustic Echo Canceller(AEC) in the presence of interference with regards to the near end speech theory and implementation aspects for acoustic echo cancellation. This paper presents the comparison between different adaptive filter usages in acoustic echo cancellation. This comparison includes the cancellation of acoustic echo generated in room using different adaptive filter like least mean square (LMS) Algorithm, Leaky Least mean square (LLMS) Algorithm, Normalized Least Mean Square (NLMS) Algorithm and Recursive Least Square (RLS) Algorithm and we also take an input sinusoidal signal and add additive white Gaussian noise and compare this results with the speech signal based on different parameters. We observe the different parameters like Echo Return Loss Enhancement (ERLE), signal to noise ratio, comparing ERLE with different filter parameters, comparing ERLE with filter length and computational complexity. We show a number of experimental results to illustrate the performance of the proposed algorithms and from the results we observe that ERLE value is high for LMS, LLMS algorithm, SNR values is high for NLMS algorithm, different filter parameters are compared with the ERLE and maximum values are estimated. The simulation part is done in MATLAB and the output results are plotted.

Due to the advancement in the technology Acoustic echo cancellation has its wide range of applications such as in mobilephone,speakerphones,hand free car fits, bluetooth accessories,multi-channel teleconferencing systems and hearing heads.The robust acoustic echo cancellation and speech enhancement technique has wide range of application in day today life in wireless and mobile systems.There are sevral number of adaptive algorithms which have different propeties, but aim is to minimize the mean square error, higher convergence rate and lesser computational complexity. The first of the analysis emphasis on the implementaion of the different adaptive algorithms by taking an input sinusoidal signal and adding aditive white guassian noise i.e (Wide band signal) to this signal and removing using echo cancellation method and comparing the results with different parameters. The second part of the thesis the adaptive filters has been applied to the Acoustic echo cancellation.The resultant signal has been obtained by convolving the speech signal(benny.wav) of the room impulse response using different adaptive algorithms like LMS,LLMS,NLMS and RLS algorithm. Experimental results shows that the RLS algorithm porformance of acoustic echo cancellation when compared to the other algorithms.When comparing the ERLE verses room impulse response the ERLE value is 27.19 for RLS and for LMS,LLMS it is 26.2 but it is 22.8 for NLMS. The average ERLE obtained from the plots for the various adaptive algorithms shows the ERLE value is maximum for RLS algorithm.ERLE verses Reverbiration time RLS has maximum ERLE value at 0.2.ERLE verses filter length RLS has maximum value.LMS filter parameter verses ERLE by varying reverbiration time 0.006 maximum value for LMS algorithm, for LLMS algorithm µ value is 0.01, for NLMS algorithm maximum β value is set to 0.3, for RLS algorithm lamda value is 1.The usefullness of any adaptive algorithm is judged by its performance in the prescence of noise.By comparing SNR values for different algorithms it is maximum for NLMS algorithm if the signal to noise ratio is maximum then the filter performance is better.The computational complexity is low for LMS algorithm and is high for RLS algorithm.So we can conclude from the results that the SNR value is high for NLMS algorithm,ERLE value is high for rls algorithm, computational complexity is less for LMS algorithm.