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.