|
 To do this, the devices would need to separate individual voices in
the room, then decode a user's brainwaves to identify the one the
user is giving the most attention, the study authors write in the
journal Science Advances.
"It comes down to the problem of hearing speech among the noise,
which is also a problem for people who have normal hearing. It can
be tiring and exhausting to focus," said senior author Nima
Mesgarani, a researcher at Columbia University in New York City.
Hearing aids typically amplify all sounds. In a noisy environment,
the challenge is separating the different sound sources and
identifying the speaker who should be amplified, Mesgarani said.
Although some devices have found ways to suppress background noises,
they can't yet effectively separate specific speakers during a
conversation.
"When you're focusing on one person who's speaking, your brain
filters out the other sources and only 'sees' that," he told Reuters
Health in a phone interview. "If it's possible to use brainwaves for
translational applications, it could change everything."
Mesgarani and colleagues write about the possibilities and
challenges around this process, called auditory attention decoding.
Importantly, smart hearing aids would need to be able to decode
quickly in a nonintrusive way, even if speakers are seated close
together.
Some research has focused on techniques that require the user to
already be familiar with a known speaker, such as a family member or
close friend, the authors note.
The study team proposes a new algorithm that could separate
unfamiliar speakers in a multi-talker situation and then compare the
spectrogram, or audio pattern, of every speaker with a
"reconstructed" spectrogram of the voice to which the listener's
brain is giving the most attention.
Researchers tested the algorithm with three epilepsy patients who
were already planning to undergo surgery to implant brain electrodes
for measuring neural activity related to their condition. All three
volunteers had normal hearing.
[to top of second column] |
During the tests, the volunteers listened to both single-talker and
multi-talker sound samples that included four stories lasting about
three minutes each. During the multi-talker experiment, they were
instructed to focus on one speaker and ignore the other.
The authors found that the matches between a spectrogram of the
voice telling the story and the reconstructed pattern from the
user's brain responses were not perfect, but they say the
differences shouldn't affect the decoding accuracy.
In addition to helping hearing-impaired users, the technology might
one day be useful to anyone trying to pick out and amplify a single
speaker in a noisy environment, they note.
"The challenge now is being able to record these brainwaves without
invasive devices, but researchers are exploring ways to put
electrodes on the head, around the ear or even inside the ear,"
Mesgarani said.
Still, wearable devices tend to have limited computational powers,
the study team writes. New hardware has been developed to implement
deep neural network models and may provide enough information to
decode a listener's focus, but this often happens at lower speeds
than preferred.
"As the technology develops, this could go beyond hearing aids and
improve the performance of voice-controlled devices such as Siri or
Alexa," said Sina Miran of the University of Maryland at College
Park, who wasn't involved in the study.
"Challenges still exist, but thanks to recent advances in machine
learning, I think we'll see smart hearing aids in the next five
years," he said in a phone interview. "Just like we're seeing
devices that can monitor sleep, stay tuned for exciting news about
hearing."
SOURCE: https://bit.ly/30HnYVa Science Advances, online May 15,
2019.
[© 2019 Thomson Reuters. All rights
reserved.] Copyright 2019 Reuters. All rights reserved. This material may not be published,
broadcast, rewritten or redistributed.
Thompson Reuters is solely responsible for this content. |
