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The real cepstrum can be computed using this equation: Rcepstrum=real(ifft(log(abs(fft(windowed_signal))))) Now do you need low pass Rcepstrum And finally apply FFT again to get the Spectral Envelope … Extract the envelope from the slashed window envelope1 = exp(2*real(fft(cep_cut1))); % spectral shape envelope2 = exp(2*real(fft(cep_cut2))); % spectral shape Find the correct factor from envelopes: …

Higher size of your FFT best the resolution of frequencies, but of course it has a cost resulting in a longer to process ! … *w; Y = fft(wdata) . . . k=k+Overlap; end …

when I read the original autotune patent few steps made me understand that everything was done in time domain (in the past, I don't know today), they didn't mention anything about overlap and add, it …

For a Spectrum Analysis using matlab FFT function you need get the first half values from your absolute FFT result and you leave to get two spikes, take a look in the Nyquist Shannon sampling theorem. … you never will be able to find 16Hz from the FFT! …

I wrote some codes just to test some filters methods, and i could see how can be hard make certain frequencies disappear completely, some methods cause ripple effects in the signal and when you apply FFT … ^(dB/20) and multiply by the FFT bin that you want eliminate, for dB attenuation -24dB my result was: My Second test EQ biquad filter by Robert Bristow-Johnson (time domain), for dB attenuation -24dB …

.* hann(length(framed)) Fourier = fft(windowed) Mag = abs(Fourier) And you need build a Window Kernel based in your Window Function! … For a Hann Window the kernel can be: Wk(k) = 0.5 * (sinc(k*(M/N)) / (1 - (k*(M/N))^2)) M=Window Size N=FFT Size Of course you can use the same size for both, N and M ! …

.* ham; Apply fft on the result of window function ham_fft = fft(windowed); ham_fft = ham_fft(1:Nsamps/2); %Discard Half of Points Make power spectrum of fft PowSpec = abs(fft(ham_fft)).^2; divide … assumes that each BIN number is the equivalent frequency (course this is nothing common was just an example), to find what bin is your frequency you need calculate: $$ BandBarks * (\frac{Fs}{length(FFT …

@robertbristow-johnson give you nice tips, i can suggest you, PSOLA algorithm (aka Keith Lent1) to pitch shift in time domain. PSOLA assumes that, if you know the pitch of a given segment, then you'l …

Here is how the algorithm works: So the steps are: Input -> apply some window (hann in the frame) -> FFT -> Get amplitude > randomize the phase -> IFFT -> overlap and add -> out Do it in a loop based …

show how to do... some steps are commented in the code, this will play one generated signal in the choose frequency, concatenate all vectors signals and play using pyaudio at the end im generating the FFT … #play all vector, maybe can be nice if you put it in one loop out = pack("%df"%len(sout), *(sout)) stream.write(out) stream.stop_stream() stream.close() pyaud.terminate() #start plot Frequencies (FFT …

can use FFT or IFFT, take a look: IFFT(log(abs(FFT(s)))) == real(FFT(log(abs(FFT(s))))) The difference is the scale representation, if do you end using FFT you need extract just the real information … , for both above equations you will get the same shape: For IFFT(log(abs(FFT(s)))): For real(FFT(log(abs(FFT(s))))): This is a cepstrum shape example from 4096 points sine in 440hz sampled at 44100hz …

product Spectrum (HPS) seems be useful for you, a feature of the HPS algorithm is multiply a number of harmonics, this give you a peak of your fundamental frequency, I believe that you will need a big FFT …

Dressler, “Sinusoidal extraction using an efficient implementation of a multi-resolution FFT,” in Proc. 9th Int. Conf. on Digital Audio Effects (DAFx-06), Montreal, Canada, Sep. 2006, pp. 247–252. …

In the same way that an low pass filter can cutoff frequency following your curve, an EQ too can be used with the same idea of time-varying

First you will need to build the frequency tables for each note scale ex. [B, A, G, F, E, D, C], you will need say what scale you will use to compare with each window pitch, of course you also have to …