Multiband processing in one pass I want to split signal in multiple bands and process each band. Number of bands is not yet determined, but it would be more then 6 and less then 11 (depending on the collected results)

As demonstrative example, let's consider common linear PCM sound data file input.wav and I want to calculate RMS per each band. Using a tool such as SoX I can extract only one band at a time (1000-2000 Hz in this example):

sox input.wav output.wav gain -n sinc 1000-2000

and pipe output to processor and extract wanted feature.  

What I would appreciate is solution to process all previously determined bands in one pass.  

It doesn't need to be a ready made program like SoX, but preferably Matlab/Octave script or Python/SciPy/NumPy and similar approach.
The most direct way to make these calculations is to design a set of filters to your specifications, then read the data out of the WAV files and run it through the filters. In Python/SciPy, you can use Python's wave module to read and write WAV files, SciPy's firwin to design a windowed sinc filter like SoX's sinc effect, and SciPy's lfilter to apply each filter. You will have to design each filter separately and call lfilter for each filter.

A simple way to approximate the energy in several bands is to first calculate the Discrete Fourier Transform (DFT) of consecutive frames, and sum up the energy in the bins corresponding to your bands of interest. The DFT approximates a signal with a sum of harmonically related sinusoids. The advantage of this technique is that it's easy to sum up several arbitrarily spaced bands. A big disadvantage is you have to apply a window to control spectral leakage. This makes the energy calculation an approximation. You can use NumPy's rfft function to calculate the Fourier Transforms.

If your bank of filters is related, for instance all the same bandwidth, or centered at harmonically or logarithmically related frequencies, there are some clever techniques that fall under the concept of filterbanks. This is more specialized, and I don't know of much pre-packaged code you could use.