- This is a camera that can see sound.
In other words, it can show you where in a scene sounds
are coming from.
How acoustic cameras work,
and what they're capable of is remarkable.
But before we get into that, I went to an anechoic chamber
to have a play with one.
Here's me playing the ukulele and talking at the same time.
It's interesting that the sound appears to switch
between coming from my mouth and coming from the ukulele,
even though sometimes both are making a sound
at the same time.
And brilliantly, this is completely analogous
to a traditional light-based camera.
What we have here is an issue of dynamic range.
If you have your camera set to automatic exposure
which your smartphone is by default,
and you shoot a video of a dim light,
then turn on a much brighter light, the camera will switch
between showing you lots of detail of the dim light
to showing you lots of detail of the bright light.
And the same thing is happening here with the ukulele.
The ukulele is louder than my voice.
So when the ukulele is being played,
the acoustic camera dials down its sensitivity
and you can no longer see my voice.
But when I stop strumming the ukulele, the camera dials up
the sensitivity, and then you can see my voice.
What's really cool is that because my voice is
in a different frequency range to the ukulele,
it's possible to isolate just my voice
and ignore the ukulele or vice versa.
And actually, this starts to hint
at one of the industrial applications for acoustic cameras.
One really noisy appliance is a vacuum cleaner.
(vacuum cleaner buzzing)
It just seems like a cacophony of sound.
But look at this spectrogram,
which is like a heat map of the different frequencies
that the vacuum cleaner is producing.
You can see that there are a handful
of really strong frequencies.
With the acoustic camera software,
you can isolate different frequencies.
So look, this frequency here we now can see
is from the tube of the vacuum cleaner.
So we now know that the tube of the vacuum cleaner resonates
at 400 hertz, and we can see that the motor
of the vacuum cleaner produces a frequency of 7,000 hertz.
And all this is really useful information
if you're trying to engineer a quieter appliance.
Another cool trick is once you have the recording,
you can use something akin to the eyedropper tool
in Photoshop to point at different parts of the recording
and see what just that part sounds like.
How cool is that?
Thanks by the way, to Ian McFarlane from AcSoft
who supply acoustic cameras in the U.K.
Check them out in the description
for all your acoustic camera needs.
They're not paying me to say that, by the way.
And thanks also to Manny and Zomi
and the Birmingham Center for Railway Research Education
at the University of Birmingham who hosted the whole thing.
Why would a railway research facility be interested
in using an acoustic camera?
Well, here's one setup, track side and as trains go by,
you can identify what wheels are making a noise
and need maintenance.
Without this kind of monitoring, often the first you know
about a problem with a train is when it breaks down
in service which causes delays and costs money.
Like imagine the lost productivity
if all these people were late for work.
I actually realized I've got this hypothesis
that I could test using the acoustic cameras.
There's this demo that I used to do
for live shows like, you know, science shows
for kids where you get an aluminum rod
like this and you pinch it in the middle
and then with a bit of rosin between your fingers
you pinch along the rod and headphone warning,
it makes a horribly high-pitched sound.
(rod screaming)
It's horrible, isn't it?
The question is, how is the rod vibrating?
Is it vibrating in the same way that like when
you twang a ruler waggling like that?
Or is it more like, you know when you pluck a guitar string?
So it's kind of like, you know it's going like that.
Well, depending on how it's vibrating,
the sound would be coming from different parts of the rod.
And you can see here that the sound is coming
from the very tips of the rod, and that's consistent
with a really unexpected vibrational mode
where the rod is essentially contracting
and then expanding and contracting and expanding.
One thing that's insanely cool about acoustic cameras
is the frame rate.
Like a typical frame rate for a camera
is 60 frames per second.
That's what I film my YouTube videos in.
But when you record sound, you are typically sampling
at about 40,000 samples per second.
And when you translate that to an acoustic camera,
that's 40,000 frames per second.
So by accident, acoustic cameras are also high-speed cameras
which means you can do things like film
what an echo looks like and then play it back
in super-slow motion.
Before we get to that though,
I want to explain how acoustic cameras work.
And actually this relates to a video I made a while back
about how humans detect where sounds are coming from,
linked to that in the card in the description,
but here's a quick summary of the salient part.
We detect where sounds are coming from based
on the timings of sound reaching our two ears.
So if a sound is coming from directly in front,
it'll reach both ears at the same time.
And that's how we know it's coming from in front.
But suppose there's a delay of 0.3 milliseconds,
that tells our brain the sound is coming
from like off to one side, like over there.
But actually it could also be coming
from over there, couldn't it?
Cuz the delay would be the same,
or like up there at an angle, or down there.
In fact, there's a whole region of possible places
the sound could be coming from.
And that region is called the cone of confusion,
though technically it's a hyperboloid of confusion.
Here's an illustration of that.
The two yellow dots are the ears
and here is the cone of possible locations of a sound
for which the delay between the two ears is the same.
So how do you reduce that surface
of possible sound locations down to a single point?
Well, the human brain has lots of clever ways of doing that.
But for a device like an acoustic camera,
you just need to add more ears.
Like here, look, imagine if I added a third ear.
Well, the same sound source will create a new cone
based on the delay between this pair of ears.
So now we know that the sound must be coming
from a point that rests on both of these cones.
In other words, somewhere along this intersecting line.
You might be thinking, well there's another pairing of ears
we haven't looked at.
These two ears make another cone.
But actually the way the maths works out
all three cones intersect at the same hyperbole.
So to really pinpoint the location of a sound,
you need a fourth ear.
A fourth ear would give us three new ear pairings
and therefore three new cones of confusion.
But if I showed all three new cones,
that would be confusing.
But just by adding one of them, we can see
that it passes through the hyperbole in just one place.
So with four ears,
you should be able to locate the exact direction
and distance to the sound source.
So why do these acoustic cameras need more than four ears?
Well, what if there were two sound sources simultaneously?
In that scenario,
two microphones would receive completely different sounds.
That's because the sounds from the two sources
would overlap differently
because of the different travel times
to the two different microphones.
That's okay for humans because humans are good
at filtering sounds in all sorts of clever ways.
But for software, that's a problem.
So instead, what the software does is analyze
individual frequencies instead of the whole waveform.
So where a human brain is trying to match up
complex wave forms, the acoustic camera is looking at
individual frequencies and the difference in phase
at the two microphones.
And it turns out that when you're looking
at different frequencies individually,
the distance between mics becomes really important.
For any given frequency,
there's an optimum distance between mics.
So by having lots of mics, you can have a range
of different distances between mics,
and so you'll be able to analyze
more frequencies more accurately.
In my previous video that I mentioned earlier,
I talk about why a high frequency sound is problematic
for microphones that are far apart
and actually it's problematic for human ears as well.
So I won't repeat myself,
but the link is in the description.
But anyway, all this explains why this acoustic camera also
from gfai tech has mics that are irregularly spaced.
If the mics replaced at regular intervals,
you would have some repeating distances
between pairs of mics.
Like all these distances are the same,
all these distances are the same.
By having a irregularly spaced mics,
you cover a greater range of distances between mics.
How acoustic cameras work is more complicated
than I've presented here.
Like for example,
having more mics gives you essentially better resolution,
but it's beyond the scope of this video.
I kind of glossed over the fact
that you get depth information from an array of microphones.
So here's a more recent model
from gfai tech that takes advantage of that.
The video camera in the middle of this one
captures depth information.
So when you're traveling around an object,
you're basically scanning it.
You can then map the sound onto that 3D object
and look around it.
How cool is that?
But the coolest thing for me
was watching an echo propagate around the room.
This is Paul.
After the initial clap, which is obviously centered
on his hands, you start to see these other hotspots flashing
around the screen where the sound is bouncing off walls.
The echo itself takes milliseconds,
but you can see all this detail in slow motion.
So flipping cool.
The echo pattern looks quite a bit different when you are
in the corner of a room.
I'm really interested in the logic
behind different business models like food delivery apps
for example, I'm hungry, I don't wanna cook
I don't wanna go out, and I want lots of choice,
food delivery apps.
The logic is incredibly straightforward
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Why am I hungry now.?
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