Date of Award
8-2018
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Electrical and Computer Engineering (Holcomb Dept. of)
Committee Member
Dr. Carl Baum and Dr. Eric Patterson, Committee Chair
Committee Member
Dr. Harlan B. Russell
Committee Member
Dr. Robert Schalkoff
Committee Member
Dr. Apoorva Kapadia
Abstract
Active Noise Canceling (ANC) is the idea of using superposition to achieve cancellation of unwanted noise and is implemented for many applications such as attempting to reduce noise in a commercial airplane cabin. One of the main traditional techniques for noise cancellation is the adaptive least mean squares (LMS) algorithm that produces the anti-noise signal, or the 180 degree out-of-phase signal to cancel the noise via superposition. This work attempts to compare several neural network approaches against the traditional LMS algorithms. The noise signals that are used for the training of the network are from the Signal Processing Information Base (SPIB) database. The neural network architectures utilized in this paper include the Multilayer Feedforward Neural Network, the Recurrent Neural Network, the Long Short Term Neural Network, and the Convolutional Neural Network. These neural networks are trained to predict the anti-noise signal based on an incoming noise signal. The results of the simulation demonstrate successful ANC using neural networks, and they show that neural networks can yield better noise attenuation than LMS algorithms. Results show that the Convolutional Neural Network architecture outperforms the other architectures implemented and tested in this work.
Recommended Citation
Park, Samuel Kyung Won, "Comparison of Neural Networks and Least Mean Squared Algorithms for Active Noise Canceling" (2018). All Theses. 2920.
https://tigerprints.clemson.edu/all_theses/2920