The two widespread concepts of noise reduction algorithms could be observed are spectral noise subtraction and adaptive filtering. They have the disadvantage that there is no parameter to distinguish between the speech and the noise components of same frequency. In this paper, an intelligent controller, BELBIC, based on mammalian limbic Emotional Learning algorithms is used for increasing the speech quality from a noisy environment. Here the learning ability to train the system to recognize and the output thus obtained would be the fundamental frequency of the speech spectrum thus reducing the noise level to minimum. The parameters on which the reduction of noise from the input speech spectrum depends have also been studied. The real time implementations have been done using Simulink and the results of the analysis thus obtained are included in the end.
Copyrights (c) 2012 Computer Science Journals. All rights reserved.
Best viewed at 1152 x 864 resolution. Microsoft Internet Explorer.
Copyrights & Usage: Articles published by CSC Journals are Open Access. Permission to copy and distribute any other content, images, animation and other parts of this website is prohibited. CSC Journals has the rights to take action against individual/group if they are found victim of copying these parts of the website.