New ways to see music (with color! and fire!)

Designer Jared Ficklin creates wild visualizations that let us see music, using color and even fire (a first for the TED stage) to analyze how sound makes us feel. He takes a brief digression to analyze the sound of a skatepark -- and how audio can clue us in to developing creativity.

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)