字幕列表 影片播放 列印英文字幕 MALE SPEAKER: Welcome. It's my pleasure to introduce Gil Weinberg. He's a professor at Georgia Tech for the Center of Music and Technology. And I went to Georgia Tech a long, long time ago and we didn't have a music department back then. So when I visited recently, the folks I was meeting with in ECE sent me over to meet Gil and he's doing some really cool stuff with interactive computing and musicianship. So building machines that you can interact with, not just from a dialogue perspective, like the kind of thing I'm thinking about, but more from a music perspective where you want to jam and do some jazz improvisation with a robot. And he's going to talk about that in many other things today. So welcome. GIL WEINBERG: Thank you. [APPLAUSE] Thank you. Thank you for coming. So I'm going to talk about three or four main projects. Only three of them I showed in these slides. The last one is a surprise. We did the project which we just finished and I have some very, very fresh new slides. The reason I started to be interested in robotic musicianship is that I'm a musician before I became interested in computation or in robotics, I was a musician and still am. And I was always fascinated by playing in a group. By being constantly ready to change what I'm doing. By trying to build on what other people are playing in real time. Improvising in a group, a visual cues, auditory cues, of course. So when I started to be interested in robotics, I wanted to capture this experience. Before I show you some of the efforts that I did, maybe I'll show a short clip of me playing with a trumpet player and trying to see the kind of experiences that I was trying to recreate. [VIDEO PLAYBACK] [JAZZ MUSIC] [END PLAYBACK] So I think you've seen a lot of eye contact, trying to build a motif that I'm not sure what is going to be and trying to create something interesting with them back and forth. And the first robots that I tried to develop it was building on these ideas. But what I wanted to do is to have the robot understand music like humans do. The big idea that I started with, was to create robots that listen like humans, but improvise like machines. Because I felt that if I want to push what music is about through new, novel improvisation algorithms and new acoustic playing, I first have to have connection between humans and robots, and that's why there is the listening like human part. Only then would I be able to start to make the robot play like a machine in order to create this kind of connection and relationship. I'll start with something very simple that probably many of you are familiar with. If I want a robot to understand me, maybe the first simple thing that I can make him do is understand the beat of the music that I play. And here we use auto-correlation and self-similarity algorithms. This is a piece from Bach. You see the time is both on the x and on the y. And you see that it's symmetric and by comparing the [INAUDIBLE] to the algorithm you can try to find sections that are similar and detect the beat from that. But what we try to do is to actually have it in real time. And you see here my student Scott Driscoll used this algorithm based on Davies and Plumbley from Queen Mary. And you see that's it becomes much more sophisticated because it's not just analyzing the beat in Bach or in the Beatles-- Scott is playing in real-time so he is trying to get the beat, but then with Haile, start to play with it. Scott is trying to fit what he is doing to what Haile-- Haile is a robot-- and back and forth. So it's a little more complicated you see. Sometimes they escape from the beat and get back to the beat. And here's a short example. Got the beat. Now Scott will start faster and slower. Faster , got it. So as you can see, it loses it, it gets it back. I think in a second, it will play slower which shows how the system fails. The next thing that I was trying to get is to have the robot understand other things at a high level musically. Not just the beat but concepts that we humans understand such as stability and similarity. And basically we had a huge database of rhythm generated almost randomly-- with some rules, some stochastic rules. And then we had a coefficient for stability and similarity. Whenever Haile listened to a particular beat there's some settings and coefficient for stability and similarities. At some point the robot actually decided by itself. First the human on the side can change the similarity and stability and create some output to bring rhythm back. And this similarity is based on Tanguiane from 1993, basically looking at the overlapping onset between beats. I can get more into this if you want, maybe later. And this is as similarity algorithm based on Desain and Honing. [INAUDIBLE] between adjunct intervals is what set how stable the rhythm is. This is based on music perception studies that I've been doing. And basically, there is a mathematical procedure where you compare each one of the notes to the note that comes after it. And at some point, after giving preference to one and two, which are stable, you can get for every particular rhythem-- for example, this one, the quarter quarter, two-eighths quarter-- a particular onset stability by combining all of the ratios and getting something stable. And here's a short example of Scott playing with Haile and Haile trying to understand the stability of Scott's rhythms. And then based on a curve of similarity, starting most similar then going less similar. And basically a human put a curve. But I can see a scenario where Haile could come up with a curve by itself, trying to start with something that Scott understands. Scott is playing seven quarters, and then slowly introduce new ideas. And you can see how Scott actually listens to the robot and at some point, building on what the robot is doing. So Haile is building on what Scott is doing, obviously, by looking at the stability and similarity. But at some point, Scott is almost inspired. Maybe inspired is too big of a word, but that's a goal that Scott will come up with an idea that he would come up with if he played with humans. [VIDEO PLAYBACK] [DRUMMING] That's almost a new idea. Scott is building on it. [END PLAYBACK] And this is a darbuka drum player concert. You will see how the professional darbuka player, actually from Israel, is kind of surprised. But I think his facial gestures were interesting for me because I think he was surprised for the better. And at some point you'll see how all of us are playing and Haile tries to get to beat. So we combine the stability similarity and beat detection into a drum circle. [VIDEO PLAYBACK] [DRUMMING] This is call and response. Later it will be simultaneously. [DRUMMING] And now, what it does, it listened to these two drummers, and tricked the pitch from one drummer, and the rhythm from another. And the other arm, most from pitch and the timbre of the two players. [END PLAYBACK] So we played the rhythm that one player played, and the pitch-- well, it's not really pitch, it's in the drum, but this is lower and this is higher next to the rim, and tried to create something that is really morphing between these two. Again, things that humans cannot do and maybe shouldn't do, but here something interesting can come up. Another thing that I was interested in is polyrhythm. It's very easy for a robot to do things that humans cannot. Sometimes I'll ask my student to clap. I will not ask you to clap. I'll give you an example. I think there is two main reasons here. [SPEAKING RHYTHM] This is nine. [SPEAKING RHYTHM] I don't ask you to clap but sometimes I would. It was [SPEAKING RHYTHM] seven. And then I asked my students to do the nine in one hand and the seven in another hand which I will definitely not ask you. But see how Haile here captured-- decided to record the rhythm. So it records the nine. He choose the nine and the seven, and at some point he introduced them in polyrhythmic, interesting rhythms. [VIDEO PLAYBACK] [DRUMMING] And we add more and more rhythms over it. [END PLAYBACK] And I don't know if know, but Pat Metheny had a project that he used robots in. He came to our lab and I explained it to him, I showed it to him. I said, this is something no humans can do. And he said, sure, my drummers can do it. And he can also do the four with his leg, and another three with another leg. So I think everyone can do it except maybe Pat Metheny's drummer. And here is something that's at the end of concert, it's obviously, you know, fishing for cheers. We just have a little short MIDI file where we playing with it together. Unison always works, so I'll just play this. We grabbed the nine and seven. [VIDEO PLAYBACK] [DRUMMING] And that's from a performance in Odense in Denmark. They had a robot festival. [END PLAYBACK] So the next project was Shimon. And actually I have an story about this because I put a video of Haile and Scott playing, the first one, on my website. It was before YouTube, or before I knew about YouTube. And someone grabbed it and put it on YouTube, and then CNN saw it and they asked to come and do a piece. And when they put a piece, the next day I got an email from the NSF, from the NSF director who said we saw your piece, please submit a proposal and continue to develop that. Rarely happens. Never happened since. I tried. I put so many videos-- And this is the next robot that we came up which adds multiple things. The main thing that it adds is the concept of pitch. It plays a marimba. And the second aspect it adds-- we're talking about the personal connection, gestures, visual cues-- is the head. And many people ask me, why the head? It doesn't play any music. In a second I'll show some of the utilization of what the head does. And of course it has a camera. That's the first question you get about any head, robotic head. So the name is Shimon, and here is the first example of my post-doc, Guy Hoffman.