thanks for your offer, I can not really read Math lab code and always
have a hard time
even figuring out essentials of such code
My phase vocoder already works kind of satisfactorily now as a demo in
Native Instruments Reaktor,
I do the forward FFT offline and the iFFT "just in time", that
�
i don't wanna lead you astray.� i would recommend staying with the phase
vocoder as a framework for doing time-frequency manipulation.� it **can** be
used real-time for pitch shift, but when i have used the phase vocoder, it was
for time-scaling and then we would simply
resample the
You get me intrigued with this
I actually believe that wavelets are the way to go for such things,
but, besides that anything beyond a Haar wavelet is too complicated for me
(and I just grasp that Haar very superficially of course),
I think one problem is the problem you mentioned - don't do
what you're discussing here appears to me to be about perfect reconstruction in
the context of Wavelets and Filter Banks.
there is a theorem that's pretty easy to prove that if you have complementary
high and low filterbanks with a common cutoff at 1/2 Nyquist, you can
downsample both
hm, my application has also WOLA ...
All I find is about up- and downsampling of time sequences and spectra
of the same length.
Summing adjacent bins seemed to be in correspondence with lowpass
filtering and decimation of time sequences
even though it's not the apropriate sinc filter...
gm wrote:
>This is brining up my previous question again, how do you decimate a
spectrum
>by an integer factor properly, can you just add the bins?
To decimate by N, you just take every Nth bin.
>the orginal spectrum represents a longer signal so I assume folding
>of the waveform occurs?
Yeah,
This is brining up my previous question again, how do you decimate a
spectrum
by an integer factor properly, can you just add the bins?
the orginal spectrum represents a longer signal so I assume folding
of the waveform occurs? but maybe this doesn't matter in practice for
some applications?
