Indeed. This debate is getting tiresome.
On Aug 21, 2015, at 8:59 PM, b...@bobhuff.com b...@bobhuff.com wrote:
From: Peter S peter.schoffhau...@gmail.com
To: music-dsp@music.columbia.edu
Sent: Friday, August 21, 2015 6:47 PM
Subject: Re: [music-dsp] Compensate for interpolation high
Creating a 22000 Hz signal from a 250 Hz signal by interpolation, is
*exactly* upsampling
That is not what is shown in that graph. The graph simply shows the
continuous-time frequency response of the interpolation polynomials,
graphed up to 22kHz. No resampling is depicted, or the frequency
Also, you even contradict yourself. You claim that:
1) Olli's graph was created by graphing sinc(x), sinc^2(x), and not via FFT.
2) The artifacts from the resampling would be barely visible, because
the oversampling rate is quite high.
So, if - according to 2) - the artifacts are not visible
On 21/08/2015, Ethan Duni ethan.d...@gmail.com wrote:
So you agree that the effects of resampling are not shown, and all we see
is the spectrum of the continuous time polynomial interpolators.
I claim that they are aliases of the original spectrum.
Just as you also call them:
It shows the
A sampled signal contains an infinte number of aliases:
http://morpheus.spectralhead.com/img/sampling_aliases.png
the spectrum is replicated infinitely often in both directions
These are called aliases of the spectrum. You do not need to fold
back the aliasing via resampling for them to become
Since that image is not meant to illustrate the effects of
resampling, but rather, to illustrate the effects of interpolation,
*obviously* it doesn't focus on the aliasing from the resampling.
So you agree that the effects of resampling are not shown, and all we see
is the spectrum of the
Let's repeat the same with a 50 Hz sine wave, sampled at 500 Hz, then
linearly interpolated and resampled at 44.1 kHz:
http://morpheus.spectralhead.com/img/sine_aliasing.png
The resulting alias frequencies are at: 450 Hz, 550 Hz, 950 Hz, 1050
Hz, 1450 Hz, 1550 Hz, 1950 Hz, 2050 Hz, 2450 Hz, 2550
It shows *exactly* the aliasing
It shows the aliasing left by linear interpolation into the continuous time
domain. It doesn't show the additional aliasing produced by then delaying
and sampling that signal. I.e., the images that would get folded back onto
the new baseband, disturbing the
On 21/08/2015, Ethan Duni ethan.d...@gmail.com wrote:
It shows *exactly* the aliasing
It shows the aliasing left by linear interpolation into the continuous time
domain. It doesn't show the additional aliasing produced by then delaying
and sampling that signal. I.e., the images that would
On 21/08/2015, Ethan Duni ethan.d...@gmail.com wrote:
Creating a 22000 Hz signal from a 250 Hz signal by interpolation, is
*exactly* upsampling
That is not what is shown in that graph. The graph simply shows the
continuous-time frequency response of the interpolation polynomials,
graphed up to
The details of how the graphs were generated don't really matter. The point
is that the only effect shown is the spectrum of the continuous-time
polynomial interpolator. The additional spectral effects of delaying and
resampling that continuous-time signal (to get fractional delay, for
example)
Which contains alias images of the original spectrum, which was my point.
There is no original spectrum pictured in that graph. Only the responses
of the interpolators. There is no reference to any input signal at all.
No one claimed there was fractional delay involved.
Fractional delay is a
1) Olli Niemiatalo's graph *is* equivalent of the spectrum of
upsampled white noise.
We've been over this repeatedly, including in the very post you are
responding to. The fact that there are many ways to produce a graph of the
interpolation spectrum is not in dispute, nor is it germaine to my
From: Peter S peter.schoffhau...@gmail.com
To: music-dsp@music.columbia.edu
Sent: Friday, August 21, 2015 6:47 PM
Subject: Re: [music-dsp] Compensate for interpolation high frequency signal
loss
On 22/08/2015, Ethan Duni ethan.d...@gmail.com wrote:
We've been over this
Naturally, there's going to be some jaggedness in the spectrum because
of the noise. So, obviously, that is not sinc^2 then.
So your whole point is that it's not *exactly* sinc^2, but a slightly noisy
version thereof? My point was that there are no effects of resampling
visible in the graphs.
Since you constantly derail this topic with irrelevant talk, let me
instead prove that
1) Olli Niemiatalo's graph *is* equivalent of the spectrum of
upsampled white noise.
2) Olli Niemitalo's graph does *not* depict sinc(x)/sinc^2(x).
First I'll prove 1).
Using palette modification, I extracted
On 22/08/2015, Ethan Duni ethan.d...@gmail.com wrote:
We've been over this repeatedly, including in the very post you are
responding to. The fact that there are many ways to produce a graph of the
interpolation spectrum is not in dispute, nor is it germaine to my point.
Earlier you disputed
Upsampling means, that the sampling rate increases. So if you have a
250 Hz signal, and create a 22000 Hz signal from it, that is - by
definition - upsampling.
That's *exactly* what upsampling means... You insert new samples
between the original ones, and interpolate between them (using
whatever
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