As meetings shifted online during the COVID-19 lockdown, many people found that
chattering roommates, garbage trucks and other loud sounds disrupted important conversations.
This experience inspired three University of Washington researchers,
who were roommates during the pandemic, to develop better earbuds. To enhance the
speaker’s voice and reduce background noise, “ClearBuds” use a novel microphone system
and one of the first machine-learning systems to operate in real time and run on a smartphone.
The researchers presented this project at the ACM
International Conference on Mobile Systems, Applications, and Services.
ClearBuds differentiate themselves from other wireless earbuds
in two key ways. First, ClearBuds use a dual microphone array. Microphones in
each earbud create two synchronized audio streams that provide information and allow
to spatially separate sounds coming from different directions with higher resolution.
Second, the lightweight neural network further enhances the speaker’s voice.
While most commercial earbuds also have microphones on each earbud,
only one earbud is actively sending audio to a phone at a time. With ClearBuds,
each earbud sends a stream of audio to the phone. The researchers designed Bluetooth
networking protocols to allow these streams to be synchronized within 70 microseconds of each other.
The team’s neural network algorithm runs on the phone to process the audio streams.
First it suppresses any non-voice sounds. And then it isolates and enhances any
noise that’s coming in at the same time from both earbuds — the speaker’s voice.
Because the speaker’s voice is close by and approximately equidistant from the two earbuds,
the neural network can be trained to focus on just their speech and eliminate background sounds,
including other voices. This method is quite similar to
how our own ears work. They use the time difference between sounds coming
to our left and right ears to determine from which direction a sound came from.
When the researchers compared ClearBuds with Apple AirPods Pro, ClearBuds performed better,
achieving a higher signal-to-distortion ratio across all tests.
The team also tested ClearBuds “in the wild,” by recording eight people reading from Project
Gutenberg in noisy environments, such as a coffee shop or on a busy street.
The researchers then had 37 people rate 10- to 60-second clips of these recordings.
Participants rated clips that were processed through ClearBuds’ neural
network as having the best noise suppression and the best overall listening experience.
One limitation of ClearBuds is that people have to
wear both earbuds to get the noise suppression experience.
But the real-time communication system developed
here can be useful for a variety of other applications, the team said,
including smart-home speakers, tracking robot locations or search and rescue missions.
The team is currently working on making the neural network algorithms
even more efficient so that they can run on the earbuds.