Okay, so these are the two sounds.

Clearly on the violin, it found more harmonics than on the voice.

So that means that we are only going to be able to interpolate the harmonics

of the voice.

So now what the transformation will do,

it will be interpolating this as two sets of values.

And we have three ways to interpolate the set of values.

We can interpolate the frequencies of the harmonics.

We can interpolate the magnitude of the harmonics.

Or, we can interpolate the stochastic component.

So for example, let's just have the frequencies of sound 0 of the first sound.

So let's put that at time 0 we'll have the sound, the first sound,

which we'll refer as 0.

And at n, we also have the first sound.

So basically the frequencies are of the violin.

And the magnitudes, let's say, are of the voice.

So we'll put that at time 0, we'll have 1 which are the magnitudes of the voice.

And at 1, we'll also have that.

And for the stochastic, well, we can just put 5815 so

we can just put a time 0.5 in between and

at times 1, we will put 0.5.

Okay, let's see what happens.

Okay, so this is a result.

And the frequencies clearly look the spacing of the violin, but

the, let's see the magnitude,

we don't see them here because we don't see the magnitude of the lines.

But let's listen to that.

[NOISE] Yeah, so clearly it sounds what it is,

it sounds a little bit the magnitude of the voice,

but at the pitch of the violin.

Now, let's go from one to another.

So if we go from all the values of the violin

to all the values of the voice,

we can just do it by putting 0011, and

again here 0011, and here 0011, okay?

And let's apply it.

Okay, and here, clearly we see that it's going from one sound, and

here from the frequency we see that there is this kind of which is

because the pitch of the voice is higher than the pitch of the violin.

So let's listen that.

[NOISE] Okay, so clearly we see this evolution.

And of course, in these, we have an envelope that we

can specify any interpolation and in any time varying fashion.

So we could have quite sophisticated interpolation envelopes.

Clearly, this is very different from the short time for a transform that we did.

So okay, let's finish this.

And basically we have talked about a transformation,

the morphing, using the harmonic plus stochastic model.

That's within the SMS tools.

And clearly it's a different type of morphing.

It has different possibilities than the SDFT.

We can now interpolate basically every set of parameters.

And obtain any sound in between.

So even though we are using the same term, morphing, the model has a big

impact on the possibilities that the technique offers and

what we can do with this idea of interpolating between two sounds.