IMPLEMENTATION OF NOISE CANCELLATION WITH HARDWARE DESCRIPTION LANGUAGE

• Main Author: SUN, LEE KUANG
• Format: Final Year Project
• Language: English
• Published: Universiti Teknologi Petronas 2006
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://utpedia.utp.edu.my/7006/1/2006%20-%20IMPLEMENTATION%20OF%20NOISE%20CANCELLATION%20WITH%20HARDWARE%20DESCRIPTION%20LANGUAGE.pdf; http://utpedia.utp.edu.my/7006/

The objective of this project is to implement noise cancellation technique on an FPGA using Hardware Description Language. The performance of several adaptive algorithms is compared to determine the desirable algorithm used for adaptive noise cancellation system. The project will focus on the implementation of adaptive filter with least-meansquares (LMS) algorithm or normalized least-mean-squares (NLMS) algorithm to cancel acoustic noises. This noise consists of extraneous or unwanted waveforms that can interfere with communication. Due to the simplicity and effectiveness of adaptive noise cancellation technique, it is used to remove the noise component from the desired signal. The project is divided into four main parts: research, Matlab simulation, ModelSim simulation and hardware implementation. The project starts with research on several noise cancellation techniques, and then with Matlab code, Simulink and FDA tool, the adaptive noise cancellation system is designed with the implementation of the LMS algorithm, NLMS algorithm and recursive-least-square algorithm to remove the interference noise. By using the Matlab code and Simulink, the noise that interfered with a sinusoidal signal and a record of music can be removed. The original signal in turns can be retrieved from the noise corrupted signal by changing the coefficient of the filter. Since filter is the important component in adaptive filtering process, the filter is designed first before adding adaptive algorithm. A Finite Impulse Response (FIR) filter is designed and the desired result of functional simulation and timing simulation is obtained through ModelSim and Integrated Software Environment (ISE) software and FPGA implementation. Finally the adaptive algorithm is added to the filter, and implemented in the FPGA. The noise is greatly reduced in Matlab simulation, functional simulation and timing simulation. Hence the results of this project show that noise cancellation with adaptive filter is feasible.