Wavelets and bank filters
Filter banks are one way implement specific discretized versions of a given continuous wavelet transform (or a given SFTF). Their performance depends a lot on how one designs wavelet voices and their subsampling. 

if you to the dyadic version (or DWT), then the highest subband spans a $[1/2,1]$ (in reduced frequencies), the next one $[1/4,1/2]$ and so on. So the  frequency resolution is usually poor on the highest subband. Alternatives consist in further dividing each subband (wavelet packets), or in already splitting the data in $M$ subbands instead of $2$ ($M$-band wavelets), or a combination of the two.

Last, the filter banks are quite generic, so nothing prevents your to use an augmented one, with both wavelet-filters and STFT-filers.