1 00:00:00,005 --> 00:00:05,525 Module 10.3, examples of start-ups that use signal processing as a core 2 00:00:05,525 --> 00:00:09,502 technology. Earlier on in this class somebody asked 3 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 4 00:00:13,012 --> 00:00:18,276 start-up and what sort of start-ups? So this brought us to think, well maybe 5 00:00:18,276 --> 00:00:23,550 we can describe a few start-ups that came out of research done in the lab. 6 00:00:23,550 --> 00:00:28,230 And our four that we discuss here, there are actually more that are active, but 7 00:00:28,230 --> 00:00:32,406 four will be discussed here are Illusonic, Quividi, Sensorscope and 8 00:00:32,406 --> 00:00:36,615 Vidinoti. So the first start up I want to discuss 9 00:00:36,615 --> 00:00:40,435 is called Illusonic. It was started by Cristof Faller who did 10 00:00:40,435 --> 00:00:43,815 his PhD thesis on a time as a [INAUDIBLE], and was interested in 11 00:00:43,815 --> 00:00:51,060 acoustical signal processing. And in particular in multi channel audio. 12 00:00:51,060 --> 00:00:54,024 So if you do acoustical signal processing, there are questions like 13 00:00:54,024 --> 00:00:57,670 beamforming, echo control, we just discussed this earlier. 14 00:00:57,670 --> 00:00:59,458 We used a project of can you hear, the shape of a room. 15 00:00:59,458 --> 00:01:03,658 When you want to do spatial audio processing, you want to generate audio 16 00:01:03,658 --> 00:01:08,942 for many channels. Either for headphones or for multichannel 17 00:01:08,942 --> 00:01:14,024 loudspeaker systems, you may want to do upmix or you take a stereo signal and you 18 00:01:14,024 --> 00:01:20,440 would like to render it as a 5:1 signal or as a 17:1 signal. 19 00:01:20,440 --> 00:01:25,840 And, there are tools, of course, where, you can use signal processing techniques, 20 00:01:25,840 --> 00:01:33,070 for example, to de-noise Music or de-reverb, recording of, person singing. 21 00:01:34,110 --> 00:01:38,335 So the tools that are used at Illusonic are classic digital signal processing 22 00:01:38,335 --> 00:01:42,820 tools, plus what was discussed briefly, when I talk about the class on audio and 23 00:01:42,820 --> 00:01:49,142 acoustic signal processing. Again, perceptual models are extremely 24 00:01:49,142 --> 00:01:52,854 important because the human auditory system is a very sophisticated signal 25 00:01:52,854 --> 00:01:56,856 processing device, and if you try to fool that device you better need to understand 26 00:01:56,856 --> 00:02:04,324 how it works. Here is an example of cool application, 27 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 28 00:02:09,380 --> 00:02:16,410 would like to listen to. So the home cinema has actually in this 29 00:02:16,410 --> 00:02:21,435 case one, two, three, four, five, six, seven, eight, nine plus probably two base 30 00:02:21,435 --> 00:02:26,310 booster somewhere, so it's probably an eleven channel system, so you would do 31 00:02:26,310 --> 00:02:34,460 enough mix from a stereo signal, let's say from your MP3 player... 32 00:02:34,460 --> 00:02:38,051 To this eleven channel spatial audio system, and you would like to make it so 33 00:02:38,051 --> 00:02:42,000 that it sounds really like you're in the concert hall. 34 00:02:42,000 --> 00:02:44,912 And so even sony has a very cool technology to do this, and not only do 35 00:02:44,912 --> 00:02:47,928 they have the technology, they actually sell a box that will do this at 36 00:02:47,928 --> 00:02:55,182 professional quality level. So the company is it's a small company 37 00:02:55,182 --> 00:02:59,403 about five people, half a dozen people, it licenses technology, state of the art 38 00:02:59,403 --> 00:03:05,775 stuff, to other, companies, and it has custom technologies that it develops. 39 00:03:05,775 --> 00:03:09,935 For specific applications and as I mentioned it has this very cool Immersive 40 00:03:09,935 --> 00:03:14,160 Audio Processor that was just launched this year and please visit our website 41 00:03:14,160 --> 00:03:18,060 and see this cool stuff and if you want to buy one of these Immersive Audio 42 00:03:18,060 --> 00:03:24,660 Processors, I can tell you it sounds incredibly beautiful. 43 00:03:27,010 --> 00:03:29,050 The next company I want to describe is Quividi. 44 00:03:29,050 --> 00:03:32,634 Now this is a very important company in its class because its a company of Palo 45 00:03:32,634 --> 00:03:36,794 Prandoni. So when he's not teaching on Coursera and 46 00:03:36,794 --> 00:03:41,530 playing his his guitar To explain signal processing. 47 00:03:41,530 --> 00:03:46,173 He's actually the CTO of a company in Paris, called Quividi. 48 00:03:46,173 --> 00:03:49,935 And Quividi does a full length thing in environments where you have cameras and 49 00:03:49,935 --> 00:03:54,017 you have digital signage. So we have advertisements on screens or 50 00:03:54,017 --> 00:03:57,702 you have information on screens, then Quividi clearly allows you to monitor who 51 00:03:57,702 --> 00:04:01,160 is actually watching what you are showing. 52 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 53 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 54 00:04:08,216 --> 00:04:12,908 people are guys. It will make some statistics, how long 55 00:04:12,908 --> 00:04:16,604 the people actually watch for example in advertisement, where they look on the 56 00:04:16,604 --> 00:04:21,547 screen and so on. And this entire system is distributed in 57 00:04:21,547 --> 00:04:26,335 the cloud, and Allows you to do a dashboard, a so-called dashboard, of how 58 00:04:26,335 --> 00:04:31,579 your advertisement is being seen in these public screens, or in the malls where the 59 00:04:31,579 --> 00:04:39,392 screens are being shown. And at latest, they have 150 networks of 60 00:04:39,392 --> 00:04:45,334 measurements that are deployed all across the world as you can see. 61 00:04:45,334 --> 00:04:50,570 And there are some very famous names that show up and so they essentially can do 62 00:04:50,570 --> 00:04:55,113 monitoring of the quality of advertisement for all of these companies 63 00:04:55,113 --> 00:04:59,964 essentially in real time and provide reports to the effectiveness of using 64 00:04:59,964 --> 00:05:08,680 advertising on screens in public spaces. Okay, that's the story of Quividi. 65 00:05:08,680 --> 00:05:11,582 It's cool technology. It uses computer vision, image 66 00:05:11,582 --> 00:05:15,222 processing, the, it also uses a lot of, you know, state of the art, algorithmic 67 00:05:15,222 --> 00:05:20,344 and machine learning technology. Please visit their website if you want to 68 00:05:20,344 --> 00:05:26,200 know more about this one. The third company is called Sensorscope. 69 00:05:26,200 --> 00:05:30,808 It grew out of all the efforts of doing environmental monitoring and various 70 00:05:30,808 --> 00:05:34,929 projects here at DPFL. So, if you want to do environmental 71 00:05:34,929 --> 00:05:38,346 monitoring, it's cool if you can do real time visualization of what is happening 72 00:05:38,346 --> 00:05:42,204 there. an application where people are very 73 00:05:42,204 --> 00:05:46,551 interesting is so-called precision agriculture, so we want to control the 74 00:05:46,551 --> 00:05:51,230 quality, let's say for example, of water systems. 75 00:05:51,230 --> 00:05:55,991 you also want to detect you know, certain weather patterns and so on and then 76 00:05:55,991 --> 00:06:00,729 optimize crop production thanks to this monitoring. 77 00:06:01,750 --> 00:06:06,175 So the company does large scale sensor networks, deployments and data 78 00:06:06,175 --> 00:06:10,370 management. So you need wireless sensor networks. 79 00:06:10,370 --> 00:06:14,470 So these are small stations that talk to each other in an ad hoc fashion. 80 00:06:14,470 --> 00:06:17,760 So self organize sensor networks. Then you need signal and image 81 00:06:17,760 --> 00:06:20,314 processing. The usual stuff that you have learned 82 00:06:20,314 --> 00:06:22,832 here in the class. And of course radio communication 83 00:06:22,832 --> 00:06:27,595 technology. So here would be a typical example. 84 00:06:27,595 --> 00:06:32,282 you build a monitoring station. We have seen such monitoring stations at 85 00:06:32,282 --> 00:06:37,010 class when we have discussed sampling issues with respect to rain monitoring. 86 00:06:37,010 --> 00:06:42,210 So you take state-of-the-art, off-the-shelf sophisticated monitoring 87 00:06:42,210 --> 00:06:46,260 communication and so on. You build sensor stations. 88 00:06:46,260 --> 00:06:49,710 You deploy them in a self-organized network. 89 00:06:49,710 --> 00:06:52,870 Then from a bay station you talk to the cloud. 90 00:06:52,870 --> 00:06:56,814 On the cloud, you have all this data, and people that are interested in monitoring 91 00:06:56,814 --> 00:07:00,410 this sort of deployment get access, privileged access to this data and can 92 00:07:00,410 --> 00:07:04,006 take statistics and, you know, decide what to do, for example, about their 93 00:07:04,006 --> 00:07:12,060 precision agriculture project. It's a small company, half a dozen 94 00:07:12,060 --> 00:07:16,200 people, and probably its main market is precision all, agriculture, even if it 95 00:07:16,200 --> 00:07:19,920 started, also from a, academic point of view, mostly about environmental 96 00:07:19,920 --> 00:07:25,298 monitoring. And you can watch their website here, you 97 00:07:25,298 --> 00:07:29,453 can also watch all the data that is online at climaps.com. 98 00:07:29,453 --> 00:07:33,297 So all the deployments that have ever been done by the company and by the lab 99 00:07:33,297 --> 00:07:37,513 are actually available here on, on this website, and you can also use this data 100 00:07:37,513 --> 00:07:41,605 and you know, do some further signal processing if you're actually interested 101 00:07:41,605 --> 00:07:49,652 by this topic. The fourth company here is called 102 00:07:49,652 --> 00:07:53,518 Vidinoti. It's a recent start up from the lab and 103 00:07:53,518 --> 00:07:56,998 it works in augmented reality, in particle augmented reality on mobile 104 00:07:56,998 --> 00:08:00,693 devices. So, the core technologies image 105 00:08:00,693 --> 00:08:05,717 recognition, computer vision. But in a ways that is robust and then to 106 00:08:05,717 --> 00:08:10,681 also do all these processing in real time on small devices, like mobile phones and 107 00:08:10,681 --> 00:08:15,061 decide how much processing you do on a mobile phone, how much you do in the 108 00:08:15,061 --> 00:08:23,188 Cloud or on the server. And Vidinoti has a bunch of state of the 109 00:08:23,188 --> 00:08:27,270 art. Algorithms on the one side, to do 110 00:08:27,270 --> 00:08:31,716 tracking and recognition, and also a number of cool ideas on how to do 111 00:08:31,716 --> 00:08:37,180 augmented reality based on these methodologies. 112 00:08:40,510 --> 00:08:44,330 So, the technology is essentially cloud-based. 113 00:08:44,330 --> 00:08:48,478 But there is an iPhone application that you can download, and then you can 114 00:08:48,478 --> 00:08:52,966 annotate your favorite pictures or newspapers or whatever with augmented 115 00:08:52,966 --> 00:08:57,590 reality, and this is actually being used in particular in the newspaper industry 116 00:08:57,590 --> 00:09:01,806 to sort of bring digital content in a, you know, in a funny or attractive way 117 00:09:01,806 --> 00:09:08,815 onto a medium. Mainly the newspaper that is being 118 00:09:08,815 --> 00:09:12,280 challenged by, of course by Internet currently. 119 00:09:12,280 --> 00:09:17,920 It is a small company, less than 10 people currently. 120 00:09:17,920 --> 00:09:22,130 It has you know, a strong research and development. 121 00:09:22,130 --> 00:09:26,486 It has also a strong intellectual portfolio based on patterns that has been 122 00:09:26,486 --> 00:09:30,689 generated over the year around Augmented Reality. 123 00:09:32,240 --> 00:09:36,022 And if you want to know more, here's a web site, and here is interactive 124 00:09:36,022 --> 00:09:40,300 application for the iPhone currently, it will be ported to Android within a couple 125 00:09:40,300 --> 00:09:47,134 of months as well. So, these were example of start-ups that 126 00:09:47,134 --> 00:09:50,704 used state of the art signal processing, image processing, computer vision, and so 127 00:09:50,704 --> 00:09:55,842 on. And bring it to the real world, in very 128 00:09:55,842 --> 00:10:02,178 concrete applications, from audio, to sensor networks, to augmented reality, to 129 00:10:02,178 --> 00:10:07,660 monitoring of audience in, advertising.