ClearBuds: Wireless Binaural Earbuds for Learning-Based Speech Enhancement
Ishan Chatterjee, Maruchi Kim, Vivek Jayaram, Shyamnath Gollakota, Ira, Kemelmacher-Shlizerman, Shwetak Patel, Steven M. Seitz

TL;DR
ClearBuds introduces a novel wireless binaural earbud system with a lightweight neural network for real-time speech enhancement, achieving high synchronization and noise suppression in diverse environments, suitable for mobile devices.
Contribution
The paper presents a new hardware design and a dual-channel neural network architecture that enables real-time speech enhancement on wireless earbuds with mobile device integration.
Findings
Achieves synchronization error less than 64 microseconds.
Neural network runs in 21.4 milliseconds on a mobile phone.
Improves speech quality and noise suppression in real-world scenarios.
Abstract
We present ClearBuds, the first hardware and software system that utilizes a neural network to enhance speech streamed from two wireless earbuds. Real-time speech enhancement for wireless earbuds requires high-quality sound separation and background cancellation, operating in real-time and on a mobile phone. Clear-Buds bridges state-of-the-art deep learning for blind audio source separation and in-ear mobile systems by making two key technical contributions: 1) a new wireless earbud design capable of operating as a synchronized, binaural microphone array, and 2) a lightweight dual-channel speech enhancement neural network that runs on a mobile device. Our neural network has a novel cascaded architecture that combines a time-domain conventional neural network with a spectrogram-based frequency masking neural network to reduce the artifacts in the audio output. Results show that our…
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