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...
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| Format: | Thesis |
| Language: | English English English |
| Published: |
2004
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| Online Access: | http://eprints.uthm.edu.my/8631/ 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 |
| _version_ | 1848889454306000896 |
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| author | Saon, Sharifah |
| author_facet | Saon, Sharifah |
| author_sort | Saon, Sharifah |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T20:26:26Z |
| format | Thesis |
| id | uthm-8631 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English English English |
| last_indexed | 2025-11-15T20:26:26Z |
| publishDate | 2004 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-86312023-05-02T02:11:57Z http://eprints.uthm.edu.my/8631/ Adaptive noise cancellation by LMS algorithm Saon, Sharifah TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution 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. 2004-04 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/8631/1/24p%20SHARIFAH%20SAON.pdf text en http://eprints.uthm.edu.my/8631/2/SHARIFAH%20SAON%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/8631/3/SHARIFAH%20SAON%20WATERMARK.pdf Saon, Sharifah (2004) Adaptive noise cancellation by LMS algorithm. Masters thesis, Kolej Universiti Teknologi Tun Hussein Onn. |
| spellingShingle | TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution Saon, Sharifah Adaptive noise cancellation by LMS algorithm |
| title | Adaptive noise cancellation by LMS algorithm |
| title_full | Adaptive noise cancellation by LMS algorithm |
| title_fullStr | Adaptive noise cancellation by LMS algorithm |
| title_full_unstemmed | Adaptive noise cancellation by LMS algorithm |
| title_short | Adaptive noise cancellation by LMS algorithm |
| title_sort | adaptive noise cancellation by lms algorithm |
| topic | TD Environmental technology. Sanitary engineering TD878-894 Special types of environment, Including soil pollution, air pollution, noise pollution |
| url | http://eprints.uthm.edu.my/8631/ 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 |