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Monday, September 01, 2014

Visual Microphones: Next-Generation Spy Technology














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Visual Microphones
Next-Generation Spy Technology








How To Translate Sight Into Sound? It's All In The Vibrations

 There are definitely limitations to the technology, Davis said, and it may not make for better sound reconstruction than other methods already in use. “Big brother probably won't be able to hear anything that anyone ever says all of a sudden,” Davis said. “But it is possible that you could use this to discover sound in situations where you couldn’t before. It’s just adding one more tool for those forensic applications.”

 Imagine someone listening in to your private conversation by filming the bag of chips sitting on the other side of the room.




Oddly specific, I know, but researchers at MIT did just that: They've created an algorithm that can reconstruct sound (and even intelligible speech) with the tiny vibrations it causes on video.







When sound hits an object, it makes distinct vibrations. “There’s this very subtle signal that’s telling you what the sound passing through is,” said Abe Davis, a graduate student in electrical engineering and computer science at MIT and first author on the paper. But the movement is tiny – sometimes as small as thousandths of a pixel on video. It’s only when all of these signals are averaged, Davis said, that you can extract sound that makes sense. By observing the entire object, you can filter out the noise.





This particular study grew out of an earlier experiment at MIT, led by Michael Rubinstein, now a postdoctoral researcher at Microsoft Research New England. In 2012, Rubinstein amplified tiny variations in video to detect things like the skin color change caused by the pumping of blood. Studying the vibrations caused by sound was a logical next step. But getting intelligible speech out of the analysis was surprising, Davis said.




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The results are certainly impressive (and a little scary). In one example shown in a compilation video, a bag of chips is filmed from 15 feet away, through sound-proof glass. The reconstructed audio of someone reciting “Mary Had a Little Lamb” in the same room as the chips isn’t crystal clear. But the words being said are possible to decipher.






In most cases, a high-speed camera is necessary to accomplish the feat. Still, at 2,000 to 6,000 frames per second, the camera used by the researchers is nothing compared to the best available on the market, which can surpass 100,000 frames per second. And the researchers found that even cheaper cameras could be used.





“It’s surprisingly possible to take advantage of a bug called rolling shutter,” Davis said. “Usually, it creates these artifacts in the image that people don’t like.” When cameras use rolling shutter to capture an image, they don’t capture one single point in time. Instead, the camera scans across the frame in one direction, picking up each row at a slightly different moment.







By doing so, the camera happens to encode information at a much higher rate than its actual frame rate. For the researchers, that meant being able to analyze vibrations that should have happened too quickly for capture on film. “It kind of turns a two-dimensional low-speed camera into a one-dimensional high-speed camera,” Davis explained.




“As a result, we can recover sounds happening at frequencies several times higher than the frame rate of the camera, which is remarkable when you consider that it’s just a complete accident of the way we make them.”




Davis and his colleagues care more about applications in scientific research. “This is a new dimension to how you can image objects,” he said. “It tells you something about how they respond physically to pressure, but instead of poking and prodding at them, all you need is to play sound at them.”


Melissa Block talks to Abe Davis, a graduate student at the Massachusetts Institute of Technology. Davis helped author a paper on a visual system to detect sound, which can recover intelligible speech from the vibrations of a potato chip bag photographed through soundproof glass.


In other experiments, they extracted useful audio signals from videos of aluminum foil, the surface of a glass of water, and even the leaves of a potted plant. The researchers will present their findings in a paper at this year’s Siggraph, the premier computer graphics conference.
“When sound hits an object, it causes the object to vibrate,” says Abe Davis, a graduate student in electrical engineering and computer science at MIT and first author on the new paper. “The motion of this vibration creates a very subtle visual signal that’s usually invisible to the naked eye. People didn’t realize that this information was there.”


Joining Davis on the Siggraph paper are Frédo Durand and Bill Freeman, both MIT professors of computer science and engineering; Neal Wadhwa, a graduate student in Freeman’s group; Michael Rubinstein of Microsoft Research, who did his PhD with Freeman; and Gautham Mysore of Adobe Research.
Reconstructing audio from video requires that the frequency of the video samples — the number of frames of video captured per second — be higher than the frequency of the audio signal. In some of their experiments, the researchers used a high-speed camera that captured 2,000 to 6,000 frames per second. That’s much faster than the 60 frames per second possible with some smartphones, but well below the frame rates of the best commercial high-speed cameras, which can top 100,000 frames per second.
Commodity Hardware

 In other experiments, however, they used an ordinary digital camera. Because of a quirk in the design of most cameras’ sensors, the researchers were able to infer information about high-frequency vibrations even from video recorded at a standard 60 frames per second. While this audio reconstruction wasn’t as faithful as that with the high-speed camera, it may still be good enough to identify the gender of a speaker in a room; the number of speakers; and even, given accurate enough information about the acoustic properties of speakers’ voices, their identities.



 The researchers’ technique has obvious applications in law enforcement and forensics, but Davis is more enthusiastic about the possibility of what he describes as a “new kind of imaging.”

 “We’re recovering sounds from objects,” he says. “That gives us a lot of information about the sound that’s going on around the object, but it also gives us a lot of information about the object itself, because different objects are going to respond to sound in different ways.” In ongoing work, the researchers have begun trying to determine material and structural properties of objects from their visible response to short bursts of sound.

 Watch how MIT researchers extract audio from the vibrations of a plant, potato-chip bag, and other objects.

 In the experiments reported in the Siggraph paper, the researchers also measured the mechanical properties of the objects they were filming and determined that the motions they were measuring were about a tenth of micrometer. That corresponds to five thousandths of a pixel in a close-up image, but from the change of a single pixel’s color value over time, it’s possible to infer motions smaller than a pixel.
Suppose, for instance, that an image has a clear boundary between two regions: Everything on one side of the boundary is blue; everything on the other is red. But at the boundary itself, the camera’s sensor receives both red and blue light, so it averages them out to produce purple. If, over successive frames of video, the blue region encroaches into the red region — even less than the width of a pixel — the purple will grow slightly bluer. That color shift contains information about the degree of encroachment.
Putting It Together

 Some boundaries in an image are fuzzier than a single pixel in width, however. So the researchers borrowed a technique from earlier work on algorithms that amplify minuscule variations in video, making visible previously undetectable motions: the breathing of an infant in the neonatal ward of a hospital, or the pulse in a subject’s wrist.


That technique passes successive frames of video through a battery of image filters, which are used to measure fluctuations, such as the changing color values at boundaries, at several different orientations — say, horizontal, vertical, and diagonal — and several different scales.

The researchers developed an algorithm that combines the output of the filters to infer the motions of an object as a whole when it’s struck by sound waves. Different edges of the object may be moving in different directions, so the algorithm first aligns all the measurements so that they won’t cancel each other out. And it gives greater weight to measurements made at very distinct edges — clear boundaries between different color values.

 The researchers also produced a variation on the algorithm for analyzing conventional video. The sensor of a digital camera consists of an array of photodetectors — millions of them, even in commodity devices. As it turns out, it’s less expensive to design the sensor hardware so that it reads off the measurements of one row of photodetectors at a time. Ordinarily, that’s not a problem, but with fast-moving objects, it can lead to odd visual artifacts. An object — say, the rotor of a helicopter — may actually move detectably between the reading of one row and the reading of the next.


For Davis and his colleagues, this bug is a feature. Slight distortions of the edges of objects in conventional video, though invisible to the naked eye, contain information about the objects’ high-frequency vibration. And that information is enough to yield a murky but potentially useful audio signal.
“This is new and refreshing. It’s the kind of stuff that no other group would do right now,” says Alexei Efros, an associate professor of electrical engineering and computer science at the University of California at Berkeley. “We’re scientists, and sometimes we watch these movies, like James Bond, and we think, ‘This is Hollywood theatrics. It’s not possible to do that. This is ridiculous.’ And suddenly, there you have it. This is totally out of some Hollywood thriller. You know that the killer has admitted his guilt because there’s surveillance footage of his potato chip bag vibrating.”


 Efros agrees that the characterization of material properties could be a fruitful application of the technology. But, he adds, “I’m sure there will be applications that nobody will expect. I think the hallmark of good science is when you do something just because it’s cool and then somebody turns around and uses it for something you never imagined. It’s really nice to have this type of creative stuff.”
MELISSA BLOCK, HOST:



Would, if you could, extract audio from silent video? How would you do it, and what would that sound like? Well, those questions have been answered by researchers at MIT and elsewhere. It's all about tiny vibrations - so small you can't see them with your eyes. But there is movement, and that movement can be converted into sound. The scientists described the process as turning visible objects, such as a house plant or a bag of potato chips into visual microphones. The first author on the paper is MIT graduate student Abe Davis, and he joins me now to explain how this works. Abe, welcome to the program.



ABE DAVIS: Hi, thanks for having me.

 BLOCK: And let me see if I've gotten this right. The idea is that sound waves will cause an object to vibrate. You are capturing that vibration on video and then - this is the tricky part - you're turning that vibration back into sound. Am I close?

 DAVIS: Yeah, that's pretty accurate.

 BLOCK: OK, well let's take a listen to what this sounds like. You played a song to a house plant, and the song sounded like this.

 (SOUNDBITE OF SONG, "MARY HAD A LITTLE LAMB")







BLOCK: And Abe, what are we hearing there?

 DAVIS: Well, that's the sound that we played out of the speaker, and, you know, it created these fluctuations in air pressure. And when those fluctuations in air pressure hit the object they move the object a very, very, very small amount. And usually we can't see that, but it turns out that it does create this very, very, very miniscule changes in video. And if you look at the video locally - if you just look at some - one part of the plant in the image that you see, then you can't really get the sound from that one part. But if you start to combine all these tiny noisy signals from all across the surface of an object, then you can start to filter out some of that noise and you can actually recover the sound that produced that motion.


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BLOCK: So when you filtered out the noise - you had a camera on the plant, you took the image of those vibrations, you captured it in a computer, and somehow with this algorithm you were able to generate sound. And here's what it sounded like.

 (SOUNDBITE OF SONG, "MARY HAD A LITTLE LAMB")

 BLOCK: So deep in their, Abe, we're hearing "Mary Had A Little Lamb." I'm not sure how it happened, but we are.





DAVIS: Yeah. That's kind of what the plant heard - or really, more accurately, what the plant felt. All sound creates these vibrations when it comes into contact with an object.

 BLOCK: Well, Abe let's listen to another example. You tried out human speech on a bag of potato chips. So here's what went in, in terms of the sound.




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(SOUNDBITE OF ARCHIVED RECORDING)

 UNIDENTIFIED MAN: Mary had a little lamb whose fleece was white as snow.

 BLOCK: And here is what you extracted.

 (SOUNDBITE OF ARCHIVED RECORDING)






UNIDENTIFIED MAN: Mary had a little lamb whose fleece was white as snow.

 BLOCK: Were you surprised when that was the result, Abe, when you first heard that?

 DAVIS: Yes. Well, sort of - I mean, that wasn't the first experiment that we did where we recovered human speech. I do remember that the first time that we recovered really clear speech, I had to keep double checking to make sure that I hadn't, you know, mixed up my signals or something.






BLOCK: Abe, what are you thinking about when you think about practical uses for what you've done here? What do you - where does take you?

 DAVIS: Most people - when they hear about this work, their mind sort of immediately goes to espionage and spying.



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BLOCK: The idea there would be you could take video that has no sound and somehow extract what the conversation was in that video?

 DAVIS: Yeah. I mean, in some situations I think that you could do that. When people hear about what we do here, it's easy to imagine that it would just kind of work in any arbitrary situation, and that's not exactly the case. I mean, what we do is limited. I think there are a lot of things that we could potentially do with it, but a lot of that is stuff that's going to hopefully be fleshed out in future work.






BLOCK: Well, Abe Davis, thanks so much for talking to us about your visual microphone.

 DAVIS: Thank you.

 BLOCK: Abe Davis is one of the MIT scientists who, along with researchers at Microsoft and Adobe, figured out how to recover audio from silent video.

Listen to the Story


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