Translator: Joseph Geni Reviewer: Morton Bast
My passions
are music, technology and making things.
And it's the combination of these things
that has led me to the hobby of sound visualization,
and, on occasion, has led me to play with fire.
This is a Rubens' tube. It's one of many I've made over the years,
and I have one here tonight.
It's about an 8-foot-long tube of metal,
it's got a hundred or so holes on top,
on that side is the speaker, and here
is some lab tubing, and it's connected to this tank
of propane.
So, let's fire it up and see what it does.
So let's play a 550-herz frequency
and watch what happens.
(Frequency)
Thank you. (Applause)
It's okay to applaud the laws of physics,
but essentially what's happening here
-- (Laughter) --
is the energy from the sound via the air and gas molecules
is influencing the combustion properties of propane,
creating a visible waveform,
and we can see the alternating regions of compression
and rarefaction that we call frequency,
and the height is showing us amplitude.
So let's change the frequency of the sound,
and watch what happens to the fire.
(Higher frequency)
So every time we hit a resonant frequency we get a standing wave
and that emergent sine curve of fire.
So let's turn that off. We're indoors.
Thank you. (Applause)
I also have with me a flame table.
It's very similar to a Rubens' tube, and it's also used
for visualizing the physical properties of sound,
such as eigenmodes, so let's fire it up
and see what it does.
Ooh. (Laughter)
Okay. Now, while the table comes up to pressure,
let me note here that the sound is not traveling
in perfect lines. It's actually traveling in all directions,
and the Rubens' tube's a little like bisecting those waves
with a line, and the flame table's a little like
bisecting those waves with a plane,
and it can show a little more subtle complexity, which is why
I like to use it to watch Geoff Farina play guitar.
(Music)
All right, so it's a delicate dance.
If you watch closely — (Applause)
If you watch closely, you may have seen
some of the eigenmodes, but also you may have seen
that jazz music is better with fire.
Actually, a lot of things are better with fire in my world,
but the fire's just a foundation.
It shows very well that eyes can hear,
and this is interesting to me because
technology allows us to present sound to the eyes
in ways that accentuate the strength of the eyes
for seeing sound, such as the removal of time.
So here, I'm using a rendering algorithm to paint
the frequencies of the song "Smells Like Teen Spirit"
in a way that the eyes can take them in
as a single visual impression, and the technique
will also show the strengths of the visual cortex
for pattern recognition.
So if I show you another song off this album,
and another, your eyes will easily pick out
the use of repetition by the band Nirvana,
and in the frequency distribution, the colors,
you can see the clean-dirty-clean sound
that they are famous for,
and here is the entire album as a single visual impression,
and I think this impression is pretty powerful.
At least, it's powerful enough that
if I show you these four songs,
and I remind you that this is "Smells Like Teen Spirit,"
you can probably correctly guess, without listening
to any music at all, that the song
a die hard Nirvana fan would enjoy is this song,
"I'll Stick Around" by the Foo Fighters,
whose lead singer is Dave Grohl,
who was the drummer in Nirvana.
The songs are a little similar, but mostly
I'm just interested in the idea that someday maybe
we'll buy a song because we like the way it looks.
All right, now for some more sound data.
This is data from a skate park,
and this is Mabel Davis skate park
in Austin, Texas. (Skateboard sounds)
And the sounds you're hearing came from eight
microphones attached to obstacles around the park,
and it sounds like chaos, but actually
all the tricks start with a very distinct slap,
but successful tricks end with a pop,
whereas unsuccessful tricks
more of a scratch and a tumble,
and tricks on the rail will ring out like a gong, and
voices occupy very unique frequencies in the skate park.
So if we were to render these sounds visually,
we might end up with something like this.
This is all 40 minutes of the recording,
and right away the algorithm tells us
a lot more tricks are missed than are made,
and also a trick on the rails is a lot more likely
to produce a cheer, and if you look really closely,
we can tease out traffic patterns.
You see the skaters often trick in this direction. The obstacles are easier.
And in the middle of the recording, the mics pick this up,
but later in the recording, this kid shows up,
and he starts using a line at the top of the park
to do some very advanced tricks on something
called the tall rail.
And it's fascinating. At this moment in time,
all the rest of the skaters turn their lines 90 degrees
to stay out of his way.
You see, there's a subtle etiquette in the skate park,
and it's led by key influencers,
and they tend to be the kids who can do the best tricks,
or wear red pants, and on this day the mics picked that up.
All right, from skate physics to theoretical physics.
I'm a big fan of Stephen Hawking,
and I wanted to use all eight hours
of his Cambridge lecture series to create an homage.
Now, in this series he's speaking with the aid of a computer,
which actually makes identifying the ends of sentences
fairly easy. So I wrote a steering algorithm.
It listens to the lecture, and then it uses
the amplitude of each word to move a point on the x-axis,
and it uses the inflection of sentences
to move a same point up and down on the y-axis.
And these trend lines, you can see, there's more questions
than answers in the laws of physics,
and when we reach the end of a sentence,
we place a star at that location.
So there's a lot of sentences, so a lot of stars,
and after rendering all of the audio, this is what we get.
This is Stephen Hawking's universe.
(Applause)
It's all eight hours of the Cambridge lecture series
taken in as a single visual impression,
and I really like this image,
but a lot of people think it's fake.
So I made a more interactive version,
and the way I did that is I used their position in time
in the lecture to place these stars into 3D space,
and with some custom software and a Kinect,
I can walk right into the lecture.
I'm going to wave through the Kinect here
and take control, and now I'm going to reach out
and I'm going to touch a star, and when I do,
it will play the sentence
that generated that star.
Stephen Hawking: There is one, and only one, arrangement
in which the pieces make a complete picture.
Jared Ficklin: Thank you. (Applause)
There are 1,400 stars.
It's a really fun way to explore the lecture,
and, I hope, a fitting homage.
All right. Let me close with a work in progress.
I think, after 30 years, the opportunity exists
to create an enhanced version of closed captioning.
Now, we've all seen a lot of TEDTalks online,
so let's watch one now with the sound turned off
and the closed captioning turned on.
There's no closed captioning for the TED theme song,
and we're missing it, but if you've watched enough of these,
you hear it in your mind's ear,
and then applause starts.
It usually begins here, and it grows and then it falls.
Sometimes you get a little star applause,
and then I think even Bill Gates takes a nervous breath,
and the talk begins.
All right, so let's watch this clip again.
This time, I'm not going to talk at all.
There's still going to be no audio,
but what I am going to do is I'm going to render the sound
visually in real time at the bottom of the screen.
So watch closely and see what your eyes can hear.
This is fairly amazing to me.
Even on the first view, your eyes will successfully
pick out patterns, but on repeated views,
your brain actually gets better
at turning these patterns into information.
You can get the tone and the timbre
and the pace of the speech,
things that you can't get out of closed captioning.
That famous scene in horror movies
where someone is walking up from behind
is something you can see,
and I believe this information would be something
that is useful at times when the audio is turned off
or not heard at all, and I speculate that deaf audiences
might actually even be better
at seeing sound than hearing audiences.
I don't know. It's a theory right now.
Actually, it's all just an idea.
And let me end by saying that sound moves in all directions,
and so do ideas.
Thank you. (Applause)