Using machine learning , researchers from MIT have arise a system that produces wakeless outcome that are so realistic they even fool human listeners .
Thenew algorithm , prepare by researchers from MIT’sComputer Science and Artificial Intelligence Laboratory , can auspicate the precise acoustic quality of a sound , and then simulate it in an extremely naturalistic room . When break down a mum video snip , such as an aim being strike by a drumstick , the system can produce a sound for the smash that ’s naturalistic enough to fool human listeners .
To make it work , Ph.D. bookman Andrew Owens and his squad apply a proficiency known as “ deep learning ” that enables computers to clean out crucial patterns bury in massive amount of raw data whole autonomously . Over the grade of several months , the research worker recorded about 1,000 videos of an estimated 46,000 sounds that represented an array of object being tally , scrape up , and prodded by a drumstick . ( The drumstick was chosen because of its power to produce consistent sounds . ) A deep - learning algorithm then examine the videos , deconstruct the sounds fit in to delivery , gaudiness , and other acoustic qualities .
“ To then portend the sound of a unexampled video , the algorithm looks at the sound properties of each flesh of that picture , and fit them to the most similar sounds in the database , ” note Owens inMIT News . “ Once the system has those bit of audio , it sew them together to create one coherent sound . ”
Incredibly , the algorithm was able to model — with a surprising degree of accuracy — the fine acoustic item of various hits , including the sounds of the drumstick on metallic element , wood , rocks , soil , and even provide . The celluloid audio were so secure that test subjects picked the fake sounds over the real I double as often . material like leaves and malicious gossip were particularly hard to secernate from the real thing , mostly because these aim tend to have less “ clean ” sounds than other objective .
This enquiry will do more than put foley artist out of work . In future , this system could ameliorate robots ’ ability to evaluate and interact with their environment .
“ A automaton could see at a pavement and instinctively know that the cementum is hard and the pasture is soft , and therefore know what would happen if they mistreat on either of them , ” said Owens . “ Being able to prognosticate audio is an crucial first step toward being able-bodied to predict the consequences of physical interaction with the existence . ”
[ MIT News , arXiv ]
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