Open Access Journal

ISSN : 2395-2717 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Electrical and Electronic Engineering(IJEREEE)

Monthly Journal for Electrical and Electronic Engineering

ISSN : 2395-2717 (Online)

Call For Paper : Vol 11, Issue 05, May 2024
Literature Review on a Class of Different Sparse Adaptive Algorithms for Echo Cancellation

Author : Ms. Dhanashri M. Kadakane 1 Prof. Mrs. A. P. Patil 2

Date of Publication :7th February 2017

Abstract: Echo is the repetition of a waveform due to reflection from points where the characteristics of the medium through which the wave propagates changes. Echo is usefully employed in sonar and radar for detection and exploration purposes. In telecommunication, echo can degrade the quality of service, and echo cancellation is an important part of communication systems. In hands-free telephony and in teleconference systems, the main aim is to provide a good free voice quality when two or more people communicate from different places. The problem often arises during the conversation is the creation of acoustic echo. This problem will cause the bad quality of voice signal and thus talkers could not hear clearly the content of the conversation, even thought lost the important information. This acoustic echo is actually the noise which is created by the reflection of sound waves by the wall of the room and the other things exist in the room. The main objective for engineers is the cancellation of this acoustic echo and provides an echo free environment for speakers during conversation. For this purpose, scientists design different adaptive filter algorithms. In the context of acoustic echo cancellation (AEC), it is shown that the level of sparseness in acoustic impulse responses can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. we propose a class of AEC algorithms that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. The proposed algorithms achieve these improvements with only a modest increase in computational complexity.

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