Open AccessDesign and Analysis of Air Quality Monitoring System
Author(s) -
R. Guruprasath,
S. Sabeenamarry,
P. Sathya,
V. Vinitha,
J. Suganthi
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1216
Subject(s) - least mean squares filter , nonlinear system , adaptive filter , computer science , euclidean distance , a priori and a posteriori , norm (philosophy) , stability (learning theory) , algorithm , noise (video) , control theory (sociology) , mathematics , artificial intelligence , machine learning , philosophy , physics , control (management) , epistemology , quantum mechanics , political science , law , image (mathematics)
In the adaptive noise cancellation (ANC) challenge, a novel least-mean-square (LMS) algorithm for filtering speech sounds has been created. It is focused on minimising the difference weight vector's squared Euclidean norm under a stability restriction specified over the a posteriori estimation error. The Lagrangian methodology was employed for this reason in order to propose a nonlinear adaptation rule described in terms of the product of differential inputs and errors, which is a generalisation of the normalised (N)LMS algorithm. The proposed approach improves monitoring ability in this sense, as shown by studies using the AURORA 2 and 3 speech databases. They include a thorough output assessment as well as a thorough comparison to regular LMS algorithms with nearly the same computational load, such as the NLMS and other recently published LMS algorithms including the updated (M)-NLMS, the error nonlinearity (EN)-LMS, or the normalised data nonlinearity (NDN)-LMS adaptation.