# ClearBuds: Wireless Binaural Earbuds for Learning-Based Speech Enhancement

arXiv: https://arxiv.org/abs/2206.13611 · HTML: https://mlsanity.com/paper/2206.13611/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2022.

## Built on by (most cited first, 7 of 7)

- [Real-Time Target Sound Extraction](https://mlsanity.com/paper/2211.02250/) (2022)
- [TRAMBA: A Hybrid Transformer and Mamba Architecture for Practical Audio and Bone Conduction Speech Super Resolution and Enhancement on Mobile and Wearable Platforms](https://mlsanity.com/paper/2405.01242/) (2024)
- [Knowledge boosting during low-latency inference](https://mlsanity.com/paper/2407.11055/) (2024)
- [Spatial Speech Translation: Translating Across Space With Binaural Hearables](https://mlsanity.com/paper/2504.18715/) (2025)
- [Enhancing XR Auditory Realism via Multimodal Scene-Aware Acoustic Rendering](https://mlsanity.com/paper/2511.11930/) (2025)
- [SonicSieve: Bringing Directional Speech Extraction to Smartphones Using Acoustic Microstructures](https://mlsanity.com/paper/2504.10793/) (2025)
- [MoXaRt: Audio-Visual Object-Guided Sound Interaction for XR](https://mlsanity.com/paper/2603.10465/) (2026)

## Abstract (arXiv)

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 wireless earbuds achieve a synchronization error less than 64 microseconds and our network has a runtime of 21.4 milliseconds 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. Project page with demos: https://clearbuds.cs.washington.edu