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So well start with the sonic visualizer.

And this is the sound I want to analyze,

it's a note of a soprano singing and let's hear that.

[SOUND] Okay, that's a quite high pitch of sound, and

it has quite a bit of a vibrato, this frequency oscillation

that is characteristic of operatic singing.

And to understand a little bit better the sound, let's open the spectrogram,

so let's open the pane on the spectrogram.

So here it is, and so we see now clearly some information of this voice sound,

we see these horizontal lines that correspond to the harmonics,

and we see this oscillation, which is basically this vibrato that is present.

To go a little bit deeper, let's open another pane with

a single spectrum of this time-veering spectrogram.

So let's first show it as in a linear scale, the horizontal axis.

Let's have lines as interpolation.

And the window let's use the same window done for the STFT.

So we'll use 1024.

Okay, and this is it.

Let's maybe make it zoom in and stretch so

that we see same things that we would see in the spectrogram, okay?

So this is one slice of the spectrogram, so

all these horizontal lines correspond to these peaks that we see here, okay?

And now maybe let's change things.

Let's change for example the window size.

If we change to 256 both analysis, okay?

Now, what we're seeing is a much smoother shape,

we are not seeing the individual harmonics, we're just seeing

an overall shape which basically correspond to what we call the formants.

The resonances of the vocal track which is what makes us be

able to distinguish between vowels for example.

So each vowel has a characteristic formant structure.

Of course if we move, things will change a little bit.

But of course the vowel remains the same so it will not change that much.

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If we go back to the analysis size that

allows us to visualize the harmonics and when we move well, we see few more changes

because the harmonics are changing more than just the formants, okay?

So now if we want to change the type of window,

we can go to the Preferences and

in the Analysis tab, let's put this away from this.

Here in the last option, we see the analysis window to be used.

Okay, curly is the blackman window.

Let's change for example to rectangular window,

which is the square that would cut the signal very abruptly.

Let's apply that.

Okay, so clearly, it doesn't look so nice.

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let's use the same values that we use before, 1024 for

both FFT size and window size with the blackman window.

And we can compute, okay, and this is basically what we saw before.

This is the 1024 samples we have started with, this is the magnitude and

phase spectrum and we see clearly the peaks corresponding to the harmonics.

I mean, here we see the phase spectrum, which we didn't see before, and

also we see the inverse of this.

So, this is the windowed signal that we generate back

by taking the inverse FFT of this spectrum.

And, of course, we can do the same thing.

We can change windows, for example,

if we change the window size to 256, and also the 50 size to 56.

In the same location, we compute, well,

we see we are, of course, taking much less samples and

the spectra are much smoother, less information there, okay?

One advantage, of course, with this interface we have is that we

can independently control the window size from the FFT size.

So we can put FFT size 1024 and

maybe a window size not that large, maybe 801.

It will compute, okay?

We are taking less samples than before but

still the frequency resolution is quite good.

And it's quite smooth because we have been doing zero padding and

so the shape of the spectrum is quite nice.

Okay, let's look at all these from the short-time Fourier transform

perspective from the spectogram.

So we will get the same sound.

Okay, and again, let's put 1024, 1024 and

the hop size has to be at least much smaller than the window

size in a way that they overlap at factor as correctly.

So for 1024 in the blackman window, at least we need one-fourth.

So let's put 256 and we compute.

Okay, and this is the result.

So we have the input signal.

The magnitude spectrogram, the phases

of the time-varying phases and the output sound and the output sound.

[SOUND] Well, it's very much the same than the original because we

have done a good reconstruction with a good overlap.

However, if this overlap is not correctly set for

example, let's put the same than the windows size for

example, 1024 and let's compute it.

Well, clearly now is something wrong in the output signal and

if we can listen to it [SOUND], okay?

Of course, we see this modulation that is at the frame rate because

we are not overlapping correctly, so every frame we see a burst of sound,

and they don't balance out by the overlap factor.

So we definitely need to have a much smaller hop size.

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And anyway, that's all I wanted to say.

Basically, I encourage you to play around with these parameters.

You can change the window size, you can change the FFT size, the hop size,

the type of window.

And in between the DFT and the STFT,

I believe you can get a good grasp of all these different parameters and

the effect they have in the visualization of the spectrogram and

also in the reconstruction of the signal.

So, let's just finish and okay, today basically,

we have used SonicVisualizer to analyze voice sound and

to visualize the spectrogram and the individual spectrum.

We have also done the same thing with the interface of the sms-tools.

And of course we have used the sound is soprano sound from freesound.

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and has allowed you to understand how useful might be to use these type of

techniques to visualize a particular sound, in this case, a soprano sound.

Of course this is just the beginning of this more complex analysis.

So in next demonstration class, we're going to analyze a more complex sound.

And we will see how we can analyze time-varying sound,

that have much more structure and

how we can use this spectrogram analysis to get some insights on that.

So thank you very much for your attention and I hope to see you next class.