Paper Info
Title
ClearBuds: wireless binaural earbuds for learning-based speech enhancement
Abstract
BSTRACTWe 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 wireless earbuds achieve a synchronization error less than 64 μs and our network has a runtime of 21.4 ms on an accompanying mobile phone. In-the-wild evaluation with eight users in previously unseen indoor and outdoor multipath scenarios demonstrates that our neural network generalizes to learn both spatial and acoustic cues to perform noise suppression and background speech removal. In a user-study with 37 participants who spent over 15.4 hours rating 1041 audio samples collected in-the-wild, our system achieves improved mean opinion score and background noise suppression. System demo video: https://youtu.be/HYu0ybjcQP
Year
DOI
Venue
2022
10.1145/3498361.3538933
Mobile Systems, Applications, and Services
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Ishan Chatterjee100.68
Maruchi Kim201.01
Vivek Jayaram342.07
Shyamnath Gollakota42788150.48
Ira Kemelmacher-Shlizerman571028.03
Shwetak N. Patel62967211.74
Steven M. Seitz78729495.13