University of Washington Researchers Introduce ‘ClearBuds’:

Caption: University of Washington researchers created ClearBuds, earbuds that enhance the speaker’s voice and reduce background noise. Shown here, the ClearBuds hardware (round disk) in front of the 3D printed earbud enclosures.

Credit: Raymond Smith/University of Washington

High-quality sound separation and background cancellation functioning in real-time and on a mobile phone is required for real-time speech augmentation for wireless earphones. Clear-Buds connects in-ear mobile devices with cutting-edge deep learning for blind audio source separation by delivering two significant technical advances. 1) a revolutionary wireless earbud design that can function as a synchronized, binaural microphone array, and 2) a portable, lightweight dual-channel neural network for speech augmentation. The first hardware and software system from end to end, ClearBuds uses a neural network to improve speech delivered via two wireless earphones.

One of the earliest real-time, smartphone-based machine learning systems, Clearbuds uses a microphone system. Meetings migrated online during the COVID-19 shutdown, and many participants discovered that chatting with roommates, trash trucks, and other loud noises interrupted crucial discussions. This gave researchers the idea for their research.

The researchers presented their study on June 30 at the ACM International Conference on Mobile Systems, Applications, and Services.

Each ClearBuds earbud transmits an audio stream to the phone. Although most commercial earbuds also have microphones on each earbud, only one actively feeds audio to a phone at once. The researchers created Bluetooth networking protocols to enable these streams to be synced within 70 microseconds of one another.

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The team’s neural network algorithm processes the audio streams on the phone. It first mutes all noises other than speech. The speaker’s voice is isolated and enhanced if it is heard concurrently in both earphones.

The neural network can be trained to concentrate only on the speaker’s speech and block out background noise, including other voices, because the speaker’s voice is close by and roughly equidistant from the two earbuds, according to co-lead author and doctoral student in the Allen School Ishan Chatterjee. “The way your own ears function is pretty similar to this approach. They use the delay between noises reaching your left and right ears to detect which direction a sound came from.

ClearBuds outperformed Apple AirPods Pro in the study’s head-to-head comparison, attaining a superior signal-to-distortion ratio in every test.

It is astounding that people realize that the neural network must operate in less than 20 milliseconds on an iPhone, which has a tiny fraction of the processing capacity of a vast commercial graphics card generally used to run neural networks.

Participants evaluated ClearBuds’ neural network-processed clips as having the most extraordinary noise cancellation and overall listening experience. By recording eight people reading from Project Gutenberg in loud settings like a coffee shop or a busy street, the researchers also tested ClearBuds “in the wild.” 37 volunteers were then asked to rate 10- to 60-second segments from these recordings.

According to the researchers, one drawback of ClearBuds is that users must use both earbuds to enjoy noise cancellation.

However, the researchers said alternative uses for the real-time communication system might include intelligent home speakers, locating robots, or search and rescue operations.

The team is now developing even more effective neural network algorithms for earphones.


Real-time sound separation and background cancellation of the highest calibre, working on a mobile phone, is required for real-time speech augmentation for wireless earbuds. Using a neural network to improve speech broadcast through two wireless earbuds, ClearBuds is the first complete hardware and software device to do so. Clear-Buds connects in-ear mobile devices and state-of-the-art deep learning for blind audio source separation by providing two crucial technical advancements. 1) A novel wireless earbud-style that can function as a synchronized, binaural microphone array, and 2) A portable dual-channel speech augmentation neural network. Attendees at MobiSys will get the opportunity to try out their demonstration by donning our earphones and talking in a loud setting while experiencing noise suppression.

This Article is written as a summary article by Marktechpost Staff based on the research paper 'ClearBuds: Wireless Binaural Earbuds for
Learning-Based Speech Enhancement'. All Credit For This Research Goes To Researchers on This Project. Checkout the paper, project and github link.

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