If I am right, Wavelet packet decomposition (WPT) breaks a signal into various filter banks. The same thing can be done using many band pass filters.
My aim is to find the energy content of a signal with a large sapmling rate ((2000 hz) in various frequency bands like 1-200, 200-400, 400-600.
What are the advantages and disadvantages of using a WPT of band pass filters?
with wpt (or dwt indeed) you have quadrature mirror filters that will ensure that if you add up all the reconstructed signals in the last level (the leaves) of the wpt tree you get exactly the original signal except for the math processor finite word length aproximations. The algorithm is pretty fast.
Moreover if your signal is non-stationary you can gain the time-frequency localization although this will drastically decrease as you go down on the tree (inverted).
The other aspect is that if yoy are lucky to get a wavelet that correlates well with the non stationary components of your signal the transform will map this components more efficiently.
For you application firstly see how many levels you have to go down in the wpt tree to go from your sampling frequency to the desired freq intervals, you may not get excately 200-400, 400-600 etc,the downer you go in the tree the more accurate are the feq limits, and you may have to join nodes to get your bands.
