A matlab toolbox for subband adaptive filtering

• Main Author: Lee, Celestine Si Hui.
• Other Authors: Gan Woon Seng
• Format: Final Year Project
• Language: English
• Published: 2010
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Applications of electronics
• Online Access: http://hdl.handle.net/10356/39468
• Institution: Nanyang Technological University

In the 21st century, the rapid advancement of technology has allowed for many applications to tap on the benefits of enhanced sound quality. This necessitates the need for researchers to explore ways to perform filtering so as to remove disturbance of noise statics or any unwanted signals. Such unwanted signals usually originate from the equipment itself, or from the surrounding environment. Based on current technology, there has been significant development in the area of Adaptive Filtering with regards to signal processing. Many time domain algorithms have been widely applied to conduct noise cancellation. However, Subband Adaptive Filtering has become the latest innovation that will further improve the quality of filtering. Subband Adaptive Filtering makes use of filter banks to separate the input into multiple signals. These signals will each undergo adaptive filtering to filter out unwanted noise, and the various outputs will then be recombined to form the desired fullband output signal. The advantages of Subband Adaptive Filtering are the increased convergence speed as well as reduction in computational complexity of the signals. However, some associated disadvantages include a decorrelating effect as well as a longer delay time depending on the decimation rate. Both time domain and Subband Adaptive Filtering have varying advantages and disadvantages when used in different situations. The creation of a user-friendly Graphical User Interface on MATLAB that includes both types of filtering algorithms will allow researchers to conveniently compare the resultant signals produced through the application of different algorithms under different environments, hence enabling them to draw a conclusion on the most suitable algorithm for the investigated application.