Adaptive noise cancellation by LMS algorithm
The research on controlling the noise level in an environment has been the focus of many researchers over the last few years. Adaptive noise cancellation (ANC) is one such approach that has been proposed for reduction of steady state noise. In this research, the least mean square (LMS) algorithm...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 语言: | English English English |
| 出版: |
2004
|
| 主题: | |
| 在线阅读: | http://eprints.uthm.edu.my/8631/1/24p%20SHARIFAH%20SAON.pdf http://eprints.uthm.edu.my/8631/2/SHARIFAH%20SAON%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/8631/3/SHARIFAH%20SAON%20WATERMARK.pdf |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| 总结: | The research on controlling the noise level in an environment has been the
focus of many researchers over the last few years. Adaptive noise cancellation
(ANC) is one such approach that has been proposed for reduction of steady state
noise. In this research, the least mean square (LMS) algorithm using MATLAB was
implemented. Step size determination was done to determine the best step size and
effects of the rate of convergence. Sound recorder was used to record sound and
saved as .wav file. Graphical user interface (GUI) was created to make it user
friendly. The output of the analysis showed that the best step size was 0.008.
Smaller step size of 0.001 tend to lower the speed of convergence, and too big a step
size, 0.8 tend to cause the system to diverge. Analysis on synthesized data showed
that the noise reduction did not eliminate the original signal. The implementation on
actual data showed slight difference between the output and input level. In real
situation, as in theory, this technique can be used to reduce noise level from noisy
signal without reducing the characteristic of the signal. |
|---|