Multiple Processes for Least Mean Square Adaptive Algorithm on Roadway Noise Cancelling

Abstract

Noise is a problem often found in daily life. Noise also make people could not concentrate to do their work. Efforts to reduce noise have been proposed, but, due to variety of the noise’s characteristics, every noise problem requires different solution. This research aim to cancel the vehicle’s noise while maintaining the information heard. These conditions happened in the hospitals classrooms, or work room near the roadway. The vehicle’s noise change very fast, so the adaptive system is the good solution candidate for solving this problem. On the beginning, the simulation process had the trouble with the iterations. Matlab software only can execute the certain range of iteration. It could not cancel the noise, even the information becomes criptic. The problem is how to cancell the vehicle’s noise with the restriction software and still manage the important information. This research will modify the LMS adaptive algorithm so that the iteration could be done by the system and the main goal of the research could be reached. The modification of the algorithm is based on the filter length (L) used to adapt with the noise. Therefore, this research conducted simulation of the Adaptive Noise Cancelling with two process steps. The output of the first adaptive process have the.same characteristics with the noise that would be cancelled, thus the first adaptive process have the error near to zero. The second adaptive process changes the input by the output of the first process and mix the information into the noise. Error occured in the final process is the information heard as the dominant output.

Exploration of Generator Noise Cancelling Using Least Mean Square Algorithm

“…This indicates that the FFT will be very efficient if the length of the vector was a number to the power of two [5]- [6]. 6) and (7) the following [7]- [8].…”

Hybrid FLC-LMS Algorithm for Predicting Sediment Volume in the River

“…The values of d and x are targets and inputs, respectively. Both are the same data, but input x is the target that is delayed by one sample [20][21][22].…”

Investigation of Diesel’s Residual Noise on Predictive Vehicles Noise Cancelling using LMS Adaptive Algorithm

“…The correlation between residual noise and White Noise Gaussian (WNG) is important to be investigated, because WNG cannot be eliminated further. The previous proposal investigated that the noise cancelling process was still leaving the residual noise [1] while Ref. [2] examine about time-frequency domain methods for noise estimation and speech enhancement.…”

“…LMS Adaptive algorithm is the robust and simple algorithm so that it can be applied to the complex signal. The noise from the vehicles were quite complicated, because the noise change fluctuations or not static [1]. The simplest structure, Linear Combiner, can be executed simply.…”

“…The LMS Adaptive Algorithm can be shown on the equation (2). Equation 2is used to find the correct weight which used on equation (1). Besides two equation, needs to find the correlation of the error which is obtained from difference of noise that want to cancel and the output of the system [3].…”