Hello everyone, in this video we'll talk about our paper called
analytic versus holistic approaches
for the live search of sound presets
using graphical interpolation
I'm Gwendal Le Vaillant and I worked on this
with my colleagues Thierry Dutoit and Rudi Giot
Before discussing the experiment that
we conducted, we'll
do a short introduction on parameters mappings
and we'll divide them into two categories
The most basic one is the one-to-one parameter mapping
which consists in assigning the commands of
a controller
directly to some controlled parameters
and we are in the context of sound synthesis
This mapping can be called analytic
as described by Hunt and Kirk
and it was 20 years ago
So, it can be considered analytic because it's
quite easy for a user to analyze
the effect of the commands
and to understand what happens
when a command-a particular command-changes
The kind of opposite type of mapping
is the many-to-many mapping
and we'll focus on the two-to-many mapping actually
because the experiment was based on touch controllers
so, the commands are the x and y coordinates
of the touch point on the touchscreen
and we'll focus in particular on the
graphical interpolation of presets
so this a kind of mapping because it is based on a few presets
the interpolation is controlled by the touch point
on the touchscreen and
the mapping function outputs the
parameters on the sound synthesis
This kind of mapping can be considered holistic
because it's very hard for a user
to understand the precise link between a command
so x and y, and the effect on the controlled parameters
now let's talk about the experiment
that was conducted
so what we did was a comparison
between a pure analytical interface
based on sliders
and a more holistic interface
based on polygons
that represented presets
and so an interpolation was computed
automatically between these presets
when the user moved a point on the touch screen
both representations were displayed on a touch app
called Miem Play
which was developed at our lab
The subjects had to listen and search for
synthesizer sounds
in a cyclic way
we had 28 users for the final experiment
but 4 more users took part in the alpha experiment
for calibrating the parameters of the experiment
so the experiment consisted in
20 cycles
we had 2 cycles of experiment
for each of the synth sounds
the cycles... each cycle was
divided in distinct steps
so it started with a pause of 8 seconds
then we had a listening step
of 20 seconds then another short pause
and the users could use the interface
for 35 seconds maximum
and they had to try to get as close
*as possible to* what they listened to
and finally a white noise
is supposed to erase the very short-term memory
which is called the auditory sensory memory
the performance of the subjects
*was* measured at the end of the search sub-step
and it depends only on the answer
so the performance of a subject does not depend
on how they search, actually
The performance was computed
using the parametric error
the answered and reference parameter values
and it was computed using the duration
that was required to finish the search
we'll talk more about the exact formula
for computing the performance
And now, let's look at a short video
that will demonstrate
how the experiment was going
After this demonstration
we'll look into the performance
evaluation function that was used
in the experiment
So, the performance score S
depends on the error E
and the duration D
The error is the normalized sum of
parametric errors, and we considered
that they are quite close to the psycho-acoustic errors
D is the duration of the search
We wanted to have a simple function
so we have a simple, basic linear decrease
of the score on both axes
and the 0.55 and 87 seconds values
were chosen to have an around
50% average performance
and we also wanted, of course, to reject random answers
One can rightfully ask what would be the results
that we'll present
if we used a different performance evaluation function
So, the results do not significantly change
if we use another valid evaluation function
and you can refer to the paper
or to our GitHub repository
Now, let's jump the results
and so we showed that
the interpolation method brings a clear improvement
on the performance
compared to the sliders control method
And so, we can ask now if this result was maybe foreseeable
because the sound space is actually reduced
when we use an interpolation method
and an interpolation-based controller
but some populations are more likely to think analytically
so, maybe the holistic control would not fit some people
and... it's mostly the experts, but some people
are used to think analytically about controllers
so maybe the holistic control would not be a good solution for them
but... to... we think that the most interesting result
was, actually, the influence of the expertise level of the subjects
So, we asked the subjects to estimate themselves
their expertise about sound synthesis and sound filtering
So, they had to estimate their level
between 1 and 4, based on text descriptions
We showed the interpolation allows very similar
performances across all expertise levels
So, actually, between the four groups, the difference
is not significant
The average performance seems to increase but the differences are negligible
While, for the sliders, there is a significant
between the medium levels, so 2 and 3
and the expert level 4
We think that this increase might come
from a possible saturation from our auditory working memory
because manipulating 4 different elements
in a live situation is actually quite a hard task
To conclude, what we did was comparative experiment
We compared a sliders-based interface
to an interface that computes interpolations on presets
We measured the performance of users
and they had to listen and then to search some presets
The two criteria were precision and speed
and the results were (obtained) on 28 subjects
The results showed a significant improvement of performance
when using the interpolation
So this is consistent with previous results
but to us, the most interesting result is that the interpolation
allows similar performances for all expertise levels
What we aim at doing now is the same experiment, for more parameters
and to compare different interpolation methods
If you want to explore the data more, you can refer to our GitHub repository
and you can get the app if you want to try it
Thank for watching!