Abstract
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.
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