This preview shows page 1. Sign up to view the full content.
Unformatted text preview: accuracy of the system identification by computing the weighted error power. . )) ( ( )) ( ( log 10 * * * * --= n n WSNR T T w w w w w w Show the effect of increasing the noise N (N=0.3, 0.5) from your experiments. Explain what you observe. Problem II In the class website ( http://cnel.ufl.edu under classes) you will find a time series called speech 1. This file contains a spoken sentence We were away a year ago sampled at 10 KHz, 12 bits A/D. The purpose here is also to compare the quality of LMS predictors in this time series. The difficulty is that speech is nonstationary! I would like you to study the effect of the filter length and the amount of data that you use to train the filter in the quality of the prediction. Normalize the error power by the input signal power and use this measure to compare the different predictors and windows. I suggest that you use filters of order 6 and 15. You have to address the fact that the data is not stationary....
View Full Document
This note was uploaded on 06/05/2011 for the course EEL 6502 taught by Professor Principe during the Spring '08 term at University of Florida.
- Spring '08