WEBVTT 00:00:00.005 --> 00:00:05.525 Module 10.3, examples of start-ups that use signal processing as a core 00:00:05.525 --> 00:00:09.502 technology. Earlier on in this class somebody asked 00:00:09.502 --> 00:00:13.012 in the forum, if I follow digital signal processing class, can I get a job in the 00:00:13.012 --> 00:00:18.276 start-up and what sort of start-ups? So this brought us to think, well maybe 00:00:18.276 --> 00:00:23.550 we can describe a few start-ups that came out of research done in the lab. 00:00:23.550 --> 00:00:28.230 And our four that we discuss here, there are actually more that are active, but 00:00:28.230 --> 00:00:32.406 four will be discussed here are Illusonic, Quividi, Sensorscope and 00:00:32.406 --> 00:00:36.615 Vidinoti. So the first start up I want to discuss 00:00:36.615 --> 00:00:40.435 is called Illusonic. It was started by Cristof Faller who did 00:00:40.435 --> 00:00:43.815 his PhD thesis on a time as a [INAUDIBLE], and was interested in 00:00:43.815 --> 00:00:51.060 acoustical signal processing. And in particular in multi channel audio. 00:00:51.060 --> 00:00:54.024 So if you do acoustical signal processing, there are questions like 00:00:54.024 --> 00:00:57.670 beamforming, echo control, we just discussed this earlier. 00:00:57.670 --> 00:00:59.458 We used a project of can you hear, the shape of a room. 00:00:59.458 --> 00:01:03.658 When you want to do spatial audio processing, you want to generate audio 00:01:03.658 --> 00:01:08.942 for many channels. Either for headphones or for multichannel 00:01:08.942 --> 00:01:14.024 loudspeaker systems, you may want to do upmix or you take a stereo signal and you 00:01:14.024 --> 00:01:20.440 would like to render it as a 5:1 signal or as a 17:1 signal. 00:01:20.440 --> 00:01:25.840 And, there are tools, of course, where, you can use signal processing techniques, 00:01:25.840 --> 00:01:33.070 for example, to de-noise Music or de-reverb, recording of, person singing. 00:01:34.110 --> 00:01:38.335 So the tools that are used at Illusonic are classic digital signal processing 00:01:38.335 --> 00:01:42.820 tools, plus what was discussed briefly, when I talk about the class on audio and 00:01:42.820 --> 00:01:49.142 acoustic signal processing. Again, perceptual models are extremely 00:01:49.142 --> 00:01:52.854 important because the human auditory system is a very sophisticated signal 00:01:52.854 --> 00:01:56.856 processing device, and if you try to fool that device you better need to understand 00:01:56.856 --> 00:02:04.324 how it works. Here is an example of cool application, 00:02:04.324 --> 00:02:09.380 so let's say you have your home cinema and you have a stereo recording that you 00:02:09.380 --> 00:02:16.410 would like to listen to. So the home cinema has actually in this 00:02:16.410 --> 00:02:21.435 case one, two, three, four, five, six, seven, eight, nine plus probably two base 00:02:21.435 --> 00:02:26.310 booster somewhere, so it's probably an eleven channel system, so you would do 00:02:26.310 --> 00:02:34.460 enough mix from a stereo signal, let's say from your MP3 player... 00:02:34.460 --> 00:02:38.051 To this eleven channel spatial audio system, and you would like to make it so 00:02:38.051 --> 00:02:42.000 that it sounds really like you're in the concert hall. 00:02:42.000 --> 00:02:44.912 And so even sony has a very cool technology to do this, and not only do 00:02:44.912 --> 00:02:47.928 they have the technology, they actually sell a box that will do this at 00:02:47.928 --> 00:02:55.182 professional quality level. So the company is it's a small company 00:02:55.182 --> 00:02:59.403 about five people, half a dozen people, it licenses technology, state of the art 00:02:59.403 --> 00:03:05.775 stuff, to other, companies, and it has custom technologies that it develops. 00:03:05.775 --> 00:03:09.935 For specific applications and as I mentioned it has this very cool Immersive 00:03:09.935 --> 00:03:14.160 Audio Processor that was just launched this year and please visit our website 00:03:14.160 --> 00:03:18.060 and see this cool stuff and if you want to buy one of these Immersive Audio 00:03:18.060 --> 00:03:24.660 Processors, I can tell you it sounds incredibly beautiful. 00:03:27.010 --> 00:03:29.050 The next company I want to describe is Quividi. 00:03:29.050 --> 00:03:32.634 Now this is a very important company in its class because its a company of Palo 00:03:32.634 --> 00:03:36.794 Prandoni. So when he's not teaching on Coursera and 00:03:36.794 --> 00:03:41.530 playing his his guitar To explain signal processing. 00:03:41.530 --> 00:03:46.173 He's actually the CTO of a company in Paris, called Quividi. 00:03:46.173 --> 00:03:49.935 And Quividi does a full length thing in environments where you have cameras and 00:03:49.935 --> 00:03:54.017 you have digital signage. So we have advertisements on screens or 00:03:54.017 --> 00:03:57.702 you have information on screens, then Quividi clearly allows you to monitor who 00:03:57.702 --> 00:04:01.160 is actually watching what you are showing. 00:04:01.160 --> 00:04:04.688 So if you have a bunch of people in front of this camera, it will identify also 00:04:04.688 --> 00:04:08.216 people it will say oh, here is a lady, here's ladies, you also got, a few of the 00:04:08.216 --> 00:04:12.908 people are guys. It will make some statistics, how long 00:04:12.908 --> 00:04:16.604 the people actually watch for example in advertisement, where they look on the 00:04:16.604 --> 00:04:21.547 screen and so on. And this entire system is distributed in 00:04:21.547 --> 00:04:26.335 the cloud, and Allows you to do a dashboard, a so-called dashboard, of how 00:04:26.335 --> 00:04:31.579 your advertisement is being seen in these public screens, or in the malls where the 00:04:31.579 --> 00:04:39.392 screens are being shown. And at latest, they have 150 networks of 00:04:39.392 --> 00:04:45.334 measurements that are deployed all across the world as you can see. 00:04:45.334 --> 00:04:50.570 And there are some very famous names that show up and so they essentially can do 00:04:50.570 --> 00:04:55.113 monitoring of the quality of advertisement for all of these companies 00:04:55.113 --> 00:04:59.964 essentially in real time and provide reports to the effectiveness of using 00:04:59.964 --> 00:05:08.680 advertising on screens in public spaces. Okay, that's the story of Quividi. 00:05:08.680 --> 00:05:11.582 It's cool technology. It uses computer vision, image 00:05:11.582 --> 00:05:15.222 processing, the, it also uses a lot of, you know, state of the art, algorithmic 00:05:15.222 --> 00:05:20.344 and machine learning technology. Please visit their website if you want to 00:05:20.344 --> 00:05:26.200 know more about this one. The third company is called Sensorscope. 00:05:26.200 --> 00:05:30.808 It grew out of all the efforts of doing environmental monitoring and various 00:05:30.808 --> 00:05:34.929 projects here at DPFL. So, if you want to do environmental 00:05:34.929 --> 00:05:38.346 monitoring, it's cool if you can do real time visualization of what is happening 00:05:38.346 --> 00:05:42.204 there. an application where people are very 00:05:42.204 --> 00:05:46.551 interesting is so-called precision agriculture, so we want to control the 00:05:46.551 --> 00:05:51.230 quality, let's say for example, of water systems. 00:05:51.230 --> 00:05:55.991 you also want to detect you know, certain weather patterns and so on and then 00:05:55.991 --> 00:06:00.729 optimize crop production thanks to this monitoring. 00:06:01.750 --> 00:06:06.175 So the company does large scale sensor networks, deployments and data 00:06:06.175 --> 00:06:10.370 management. So you need wireless sensor networks. 00:06:10.370 --> 00:06:14.470 So these are small stations that talk to each other in an ad hoc fashion. 00:06:14.470 --> 00:06:17.760 So self organize sensor networks. Then you need signal and image 00:06:17.760 --> 00:06:20.314 processing. The usual stuff that you have learned 00:06:20.314 --> 00:06:22.832 here in the class. And of course radio communication 00:06:22.832 --> 00:06:27.595 technology. So here would be a typical example. 00:06:27.595 --> 00:06:32.282 you build a monitoring station. We have seen such monitoring stations at 00:06:32.282 --> 00:06:37.010 class when we have discussed sampling issues with respect to rain monitoring. 00:06:37.010 --> 00:06:42.210 So you take state-of-the-art, off-the-shelf sophisticated monitoring 00:06:42.210 --> 00:06:46.260 communication and so on. You build sensor stations. 00:06:46.260 --> 00:06:49.710 You deploy them in a self-organized network. 00:06:49.710 --> 00:06:52.870 Then from a bay station you talk to the cloud. 00:06:52.870 --> 00:06:56.814 On the cloud, you have all this data, and people that are interested in monitoring 00:06:56.814 --> 00:07:00.410 this sort of deployment get access, privileged access to this data and can 00:07:00.410 --> 00:07:04.006 take statistics and, you know, decide what to do, for example, about their 00:07:04.006 --> 00:07:12.060 precision agriculture project. It's a small company, half a dozen 00:07:12.060 --> 00:07:16.200 people, and probably its main market is precision all, agriculture, even if it 00:07:16.200 --> 00:07:19.920 started, also from a, academic point of view, mostly about environmental 00:07:19.920 --> 00:07:25.298 monitoring. And you can watch their website here, you 00:07:25.298 --> 00:07:29.453 can also watch all the data that is online at climaps.com. 00:07:29.453 --> 00:07:33.297 So all the deployments that have ever been done by the company and by the lab 00:07:33.297 --> 00:07:37.513 are actually available here on, on this website, and you can also use this data 00:07:37.513 --> 00:07:41.605 and you know, do some further signal processing if you're actually interested 00:07:41.605 --> 00:07:49.652 by this topic. The fourth company here is called 00:07:49.652 --> 00:07:53.518 Vidinoti. It's a recent start up from the lab and 00:07:53.518 --> 00:07:56.998 it works in augmented reality, in particle augmented reality on mobile 00:07:56.998 --> 00:08:00.693 devices. So, the core technologies image 00:08:00.693 --> 00:08:05.717 recognition, computer vision. But in a ways that is robust and then to 00:08:05.717 --> 00:08:10.681 also do all these processing in real time on small devices, like mobile phones and 00:08:10.681 --> 00:08:15.061 decide how much processing you do on a mobile phone, how much you do in the 00:08:15.061 --> 00:08:23.188 Cloud or on the server. And Vidinoti has a bunch of state of the 00:08:23.188 --> 00:08:27.270 art. Algorithms on the one side, to do 00:08:27.270 --> 00:08:31.716 tracking and recognition, and also a number of cool ideas on how to do 00:08:31.716 --> 00:08:37.180 augmented reality based on these methodologies. 00:08:40.510 --> 00:08:44.330 So, the technology is essentially cloud-based. 00:08:44.330 --> 00:08:48.478 But there is an iPhone application that you can download, and then you can 00:08:48.478 --> 00:08:52.966 annotate your favorite pictures or newspapers or whatever with augmented 00:08:52.966 --> 00:08:57.590 reality, and this is actually being used in particular in the newspaper industry 00:08:57.590 --> 00:09:01.806 to sort of bring digital content in a, you know, in a funny or attractive way 00:09:01.806 --> 00:09:08.815 onto a medium. Mainly the newspaper that is being 00:09:08.815 --> 00:09:12.280 challenged by, of course by Internet currently. 00:09:12.280 --> 00:09:17.920 It is a small company, less than 10 people currently. 00:09:17.920 --> 00:09:22.130 It has you know, a strong research and development. 00:09:22.130 --> 00:09:26.486 It has also a strong intellectual portfolio based on patterns that has been 00:09:26.486 --> 00:09:30.689 generated over the year around Augmented Reality. 00:09:32.240 --> 00:09:36.022 And if you want to know more, here's a web site, and here is interactive 00:09:36.022 --> 00:09:40.300 application for the iPhone currently, it will be ported to Android within a couple 00:09:40.300 --> 00:09:47.134 of months as well. So, these were example of start-ups that 00:09:47.134 --> 00:09:50.704 used state of the art signal processing, image processing, computer vision, and so 00:09:50.704 --> 00:09:55.842 on. And bring it to the real world, in very 00:09:55.842 --> 00:10:02.178 concrete applications, from audio, to sensor networks, to augmented reality, to 00:10:02.178 --> 00:10:07.660 monitoring of audience in, advertising.