back to 400 samples and let's leave the rest the same.

And we compute, and now we in fact we will obtain the same thing.

It's the same thing but now the window size is small which

is sufficient, and if we play the original [SOUND] and

if we play the reconstructed [SOUND] is identical.

So we basically have captured all the relevant

information of these sinusoidal components of the sound.

Okay, now let's go to more real sound and more natural sound.

So let's close all these play windows.

And let's start again from the DFT, but let's look at a violin sound.

So there is a violin sound here in the sounds of the SMS tools.

Which is a violin with frequency B3, and we can listen to that.

[SOUND] Okay, so B3, the pitch that corresponds to the note B3,

which is lower than the A4 that we had before,

is 200 and around 46 hertz, okay?

So, in order to find the best window size,

we can compute the, for

example if we start from a humming window,

we need to compute 4 x 444,100,

divided by the frequency so 246.

Okay, so this is a lower frequency.

Therefore four periods of the sound is each larger 717 samples.

So we can put here 717, okay.

When we can be the same f50 size, and

in fact, well we can this sound a little bit longer.

So, let's put 0.5 as place to be analyzed.

And here is the result, this is not an electronic sound so

the number of periods now that we have chosen still four,

but is much more irregular than with a subtle.

So in fact here, it's even a little bit harder to see the period.

In fact, the period is like two bumps.

So this would be one period would go from here to here and another.

Then another, so it's again, four periods of the violin sound.

The spectrum is a little more complex than the one again of the subtle,

but we see clearly the harmonics.

So if we zoom a little bit better into the part

that we see as being relevant we see the first

few peaks, and these are clearly the harmonics of the sound.

But we see a lot of kind of energy or

spectral information that doesn't have this nice-looking

sort of peaks or shapes corresponding to the window.

So in fact Instead of a hamming window, it might be better to take

a smoother window that kind of can discriminate better these

kind of background residual or noise, or these sounds.

So let's use the Blackman window.

And having this, is being smoother, we need more samples.

So in fact we need six periods for this,

the main lope of the Blackman is six beam wide.

So we use the same equation to complete the window size but

multiplied by six, so now we need at least 1075 samples.

So let's put here 1075 samples, and let's compute the same way.

And now, we're seeing much better

the harmonics of the sound.

In fact, let's compare it with the previous one and

having zoom Into the same area so we zoom