10.3 - Examples of start-ups that use signal processing as a core technology

From the official description of 10.. videos:

Goodbye!

As a parting message, we prepared an extra Module (yes, a "bonus feature", just like in DVDs!) with the following purposes:

give you some pointers if you want to learn more about signal processing
show you some of the cool research topics in signal processing that are currently being pursued in our lab
show you how signal processing translates to the real world by introducing several startups founded by current and former members of our lab

Module 10.3, examples of start-ups that
use signal processing as a core

technology.
Earlier on in this class somebody asked

in the forum, if I follow digital signal
processing class, can I get a job in the

start-up and what sort of start-ups?
So this brought us to think, well maybe

we can describe a few start-ups that came
out of research done in the lab.

And our four that we discuss here, there
are actually more that are active, but

four will be discussed here are
Illusonic, Quividi, Sensorscope and

Vidinoti.
So the first start up I want to discuss

is called Illusonic.
It was started by Cristof Faller who did

his PhD thesis on a time as a
[INAUDIBLE], and was interested in

acoustical signal processing.
And in particular in multi channel audio.

So if you do acoustical signal
processing, there are questions like

beamforming, echo control, we just
discussed this earlier.

We used a project of can you hear, the
shape of a room.

When you want to do spatial audio
processing, you want to generate audio

for many channels.
Either for headphones or for multichannel

loudspeaker systems, you may want to do
upmix or you take a stereo signal and you

would like to render it as a 5:1 signal
or as a 17:1 signal.

And, there are tools, of course, where,
you can use signal processing techniques,

for example, to de-noise Music or
de-reverb, recording of, person singing.

So the tools that are used at Illusonic
are classic digital signal processing

tools, plus what was discussed briefly,
when I talk about the class on audio and

acoustic signal processing.
Again, perceptual models are extremely

important because the human auditory
system is a very sophisticated signal

processing device, and if you try to fool
that device you better need to understand

how it works.
Here is an example of cool application,

so let's say you have your home cinema
and you have a stereo recording that you

would like to listen to.
So the home cinema has actually in this

case one, two, three, four, five, six,
seven, eight, nine plus probably two base

booster somewhere, so it's probably an
eleven channel system, so you would do

enough mix from a stereo signal, let's
say from your MP3 player...

To this eleven channel spatial audio
system, and you would like to make it so

that it sounds really like you're in the
concert hall.

And so even sony has a very cool
technology to do this, and not only do

they have the technology, they actually
sell a box that will do this at

professional quality level.
So the company is it's a small company

about five people, half a dozen people,
it licenses technology, state of the art

stuff, to other, companies, and it has
custom technologies that it develops.

For specific applications and as I
mentioned it has this very cool Immersive

Audio Processor that was just launched
this year and please visit our website

and see this cool stuff and if you want
to buy one of these Immersive Audio

Processors, I can tell you it sounds
incredibly beautiful.

The next company I want to describe is
Quividi.

Now this is a very important company in
its class because its a company of Palo

Prandoni.
So when he's not teaching on Coursera and

playing his his guitar To explain signal
processing.

He's actually the CTO of a company in
Paris, called Quividi.

And Quividi does a full length thing in
environments where you have cameras and

you have digital signage.
So we have advertisements on screens or

you have information on screens, then
Quividi clearly allows you to monitor who

is actually watching what you are
showing.

So if you have a bunch of people in front
of this camera, it will identify also

people it will say oh, here is a lady,
here's ladies, you also got, a few of the

people are guys.
It will make some statistics, how long

the people actually watch for example in
advertisement, where they look on the

screen and so on.
And this entire system is distributed in

the cloud, and Allows you to do a
dashboard, a so-called dashboard, of how

your advertisement is being seen in these
public screens, or in the malls where the

screens are being shown.
And at latest, they have 150 networks of

measurements that are deployed all across
the world as you can see.

And there are some very famous names that
show up and so they essentially can do

monitoring of the quality of
advertisement for all of these companies

essentially in real time and provide
reports to the effectiveness of using

advertising on screens in public spaces.
Okay, that's the story of Quividi.

It's cool technology.
It uses computer vision, image

processing, the, it also uses a lot of,
you know, state of the art, algorithmic

and machine learning technology.
Please visit their website if you want to

know more about this one.
The third company is called Sensorscope.

It grew out of all the efforts of doing
environmental monitoring and various

projects here at DPFL.
So, if you want to do environmental

monitoring, it's cool if you can do real
time visualization of what is happening

there.
an application where people are very

interesting is so-called precision
agriculture, so we want to control the

quality, let's say for example, of water
systems.

you also want to detect you know, certain
weather patterns and so on and then

optimize crop production thanks to this
monitoring.

So the company does large scale sensor
networks, deployments and data

management.
So you need wireless sensor networks.

So these are small stations that talk to
each other in an ad hoc fashion.

So self organize sensor networks.
Then you need signal and image

processing.
The usual stuff that you have learned

here in the class.
And of course radio communication

technology.
So here would be a typical example.

you build a monitoring station.
We have seen such monitoring stations at

class when we have discussed sampling
issues with respect to rain monitoring.

So you take state-of-the-art,
off-the-shelf sophisticated monitoring

communication and so on.
You build sensor stations.

You deploy them in a self-organized
network.

Then from a bay station you talk to the
cloud.

On the cloud, you have all this data, and
people that are interested in monitoring

this sort of deployment get access,
privileged access to this data and can

take statistics and, you know, decide
what to do, for example, about their

precision agriculture project.
It's a small company, half a dozen

people, and probably its main market is
precision all, agriculture, even if it

started, also from a, academic point of
view, mostly about environmental

monitoring.
And you can watch their website here, you

can also watch all the data that is
online at climaps.com.

So all the deployments that have ever
been done by the company and by the lab

are actually available here on, on this
website, and you can also use this data

and you know, do some further signal
processing if you're actually interested

by this topic.
The fourth company here is called

Vidinoti.
It's a recent start up from the lab and

it works in augmented reality, in
particle augmented reality on mobile

devices.
So, the core technologies image

recognition, computer vision.
But in a ways that is robust and then to

also do all these processing in real time
on small devices, like mobile phones and

decide how much processing you do on a
mobile phone, how much you do in the

Cloud or on the server.
And Vidinoti has a bunch of state of the

art.
Algorithms on the one side, to do

tracking and recognition, and also a
number of cool ideas on how to do

augmented reality based on these
methodologies.

So, the technology is essentially
cloud-based.

But there is an iPhone application that
you can download, and then you can

annotate your favorite pictures or
newspapers or whatever with augmented

reality, and this is actually being used
in particular in the newspaper industry

to sort of bring digital content in a,
you know, in a funny or attractive way

onto a medium.
Mainly the newspaper that is being

challenged by, of course by Internet
currently.

It is a small company, less than 10
people currently.

It has you know, a strong research and
development.

It has also a strong intellectual
portfolio based on patterns that has been

generated over the year around Augmented
Reality.

And if you want to know more, here's a
web site, and here is interactive

application for the iPhone currently, it
will be ported to Android within a couple

of months as well.
So, these were example of start-ups that

used state of the art signal processing,
image processing, computer vision, and so

on.
And bring it to the real world, in very

concrete applications, from audio, to
sensor networks, to augmented reality, to

monitoring of audience in, advertising.