The wavelet transform is an indispensable tool for a variety of applications such as classification, compression, and estimation. One of the key properties of wavelets is the fact that they form unconditional bases for many different signal classes. Thus, most signal information in wavelet expansions is conveyed by a relatively small number of large coefficients. This property of the wavelet transform makes the use of wavelets particularly ideal in signal estimation [3].
It has been shown that wavelets can remove noise more effectively than previously used methods. This noise may be caused by several factors including disruptions in transmissions, imperfections in data collection, quality of audio recording, experimental error, etc.. Undoubtedly, the use of wavelets will continue to impact the sciences through the practical use of noise reduction.
This web site provides information on the denoising process.
Click on the following links to find out more...
What is noise?
Reducing Noise by:
Wavelet Shrinkage
Thresholding
Filtering
High and Low Pass Filters
Examples:
Denoising an Audio Signal with the Haar Wavelets
Denoising Laboratory Data
If you have questions or comments write:
John at steedj@student.gvsu.edu or
Renee at miedemar@student.gvsu.edu