Noise Cancellation In An Audio Signal With Adaptive Filters Techniques

Abstract

Noise has always been an un-favorable performer in 
any form of communication. Because noise is a 
random process that changes over time, it is 
estimated at each instant to cancel out the original 
signal. Unless a perfect noise cancellation system is 
used in voice communication, any recorded or live 
speech data signal transmitted will be useless. There 
are several noise cancellation schemes, but the 
adaptive filter is the most effective. By implementing 
the three conventional adaptive algorithms [LMS, 
JAYA] noise was eliminated significantly. The 
Simulation results indicate a higher quality of noise 
cancellation  
This paper presents a novel approach to noise 
cancellation in audio signals utilizing adaptive 
filters, specifically focusing on the Jaya algorithm 
and the Least Mean Squares (LMS) algorithm. Noise 
pollution significantly degrades audio quality, 
impacting 
various 
applications 
such 
as 
telecommunications and music production. The 
proposed method integrates the Jaya algorithm, kno 
noise cancellation using adaptive filters, specifically 
through the integration of the Jaya algorithm with 
the Least Mean Squares (LMS) algorithm. In the 
realm of audio signal processing, noise cancellation 
is a critical challenge that affects various 
applications, including telecommunications, music 
production, and hearing aids. Traditional methods 
often struggle to adapt to dynamic noise 
environments, leading to suboptimal performance.  The Jaya algorithm is a population-based 
optimization technique characterized by its 
simplicity and efficiency in finding optimal 
solutions. By leveraging the strengths of the Jaya 
algorithm, we enhance the LMS algorithm, which 
adjusts filter coefficients based on the error signal 
to minimize the difference between the desired and 
actual audio output. The adaptive nature of these 
algorithms allows for real-time updates, making 
them suitable for varying noise conditions.