The technology, called auditory attention decoding, relies on the deep neural network model of artificial intelligence. Neural networks mimic the workings of the human brain – learning on the fly and coming up with their own solutions to new problems. Using the neural network approach, the hearing aid computer actually teaches itself, over time, the best way to pluck a single voice out of a crowded room.
“Our algorithm uses deep neural networks, because they are becoming so widespread, many researchers are developing low-power specialized hardware to implement them in real time,” Mesgarani said. “Also, the modern hearing aids are able to do some of their calculation off-board – for example by syncing to your phone – which helps to manage heavy computation in such small device.”
The technology is in very early proof-of concept phase, but Mesgarani said that if all goes well the system could start showing up in commercial hearing aids within five years.
“There is no theoretical reason prohibiting the implementation of this technology in an actual hearing aid,” he said. “In fact, several hearing aid companies have already started researching this idea and expressed interest in our approach.”
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Down the line, the researcher hope to further refine and miniaturize the technology so that everything fits into a more-or-less standard hearing-aid apparatus. For one thing, Mesgarani said, that skullcap of electrodes has got to go.
“Others have shown the feasibility of decoding attention using in-ear recording — an earbud with electrodes placed on it — or a C-shaped array of electrodes that is placed around the ear, a similar shape to a conventional hearing aid,” Mesgarani said.
The research, published this week in the Journal of Neural Engineering, is a collaboration among the Columbia University Medical Center's Department of Neurosurgery, the Hofstra-Northwell School of Medicine, and Feinstein Institute for Medical Research and was funded in part by grants from the National Institutes of Health.
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