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The result is that the algorithm can accurately simulate the subtleties of different hits, from the staccato taps of a rock to the longer waveforms of rustling ivy. “Once the system has those bits of audio, it stitches them together to create one coherent sound.” “To then predict the sound of a new video, the algorithm looks at the sound properties of each frame of that video, and matches them to the most similar sounds in the database,” says Owens. (They used a drumstick because it provided a consistent way to produce a sound.)

Over several months, the researchers recorded roughly 1,000 videos of an estimated 46,000 sounds that represent various objects being hit, scraped and prodded with a drumstick. The first step to training a sound-producing algorithm is to give it sounds to study. The paper will be presented later this month at the annual conference on Computer Vision and Pattern Recognition (CVPR) in Las Vegas. The paper’s co-authors include recent PhD graduate Phillip Isola and MIT professors Edward Adelson, Bill Freeman, Josh McDermott and Antonio Torralba. Deep learning approaches are especially useful because they free computer scientists from having to hand-design algorithms and supervise their progress. The team used techniques from the field of “deep learning,” which involves teaching computers to sift through huge amounts of data to find patterns on their own. “An algorithm that models such sounds can reveal key information about objects’ shapes and material types, as well as the force and motion of their interactions with the world.” “When you run your finger across a wine glass, the sound it makes reflects how much liquid is in it,” says CSAIL PhD student Andrew Owens, who was lead author on the paper. The project represents much more than just a clever computer trick: researchers envision future versions of similar algorithms being used to automatically produce sound effects for movies and TV shows, as well as to help robots better understand objects’ properties.

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