An Acoustic Echo Cancellation System based on Adaptive Algorithm

University essay from Blekinge Tekniska Högskola/Sektionen för ingenjörsvetenskap; Blekinge Tekniska Högskola/Sektionen för ingenjörsvetenskap


Adaptive filtering technique is one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering technique is widely used in many applications, including echo cancellation, adaptive noise cancellation, adaptive beam forming and adaptive equalization. Acoustic echo is a common occurrence in today’s telecommunication systems. The distraction caused by the acoustic echo, reduces the speech quality in the communication. In the communication system acoustic echo cancellers is used works as the far-end signal is delivered to the system, it will be reproduced by the loudspeaker in the room. A microphone in the room picks up the resulting direct path sound and consequent reverberant sound as a near-end signal, The far-end signal is filtered and delayed to resemble the near-end signal, filtered far-end signal is subtracted from the near-end signal. The resultant signal represents sounds present in the room excluding any direct or reverberated sound produced by the loudspeaker. The AEC with adaptive filtering technique will more accurately enhance the speech quality in hands-free and teleconferencing communication systems. The focus is on speech enhancement of speech signal with reverberated signal in handsfree speech communication using AEC with adaptive filtering technique. There are many adaptive algorithms available in the literature for echo cancellation and every algorithm have its own properties, but the aim of algorithms using for echo cancellation is to achieve higher ERLE(amount of echo cancelled) in dB at a higher rate of convergence with low complexity. The adaptive algorithms NLMS, APA and RLS for echo cancellation were successfully implemented in MATLAB. The three algorithms for AEC are tested with simulation in three different echo occurring environments by changing microphone position, source position and room dimensions. The performance evaluation of the NLMS, APA and RLS algorithms are measured with ERLE parameter. The results show that the RLS algorithm have good performance with high rate of convergence speed but the computational complexity is high which makes it impractical in real time applications. The amount of echo cancellation with APA algorithm is higher than NLMS with less computational complexity than RLS and easy to implement in real time. The amount of echo cancellation with NLMS is low when compared to RLS and APA but it is easy to implement in real time with less computational complexity. The detailed view of the comparison results of three algorithms at three different environments are shown in section 6.

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