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Chengyi Shen

Publications and source records attributed to Chengyi Shen.

3 recordsLinked to original sources

Active noise cancellation on open-ear smart glasses

Active noise cancellation (ANC) is widely deployed on consumer headphones and earbuds to suppress environmental noise. However, existing ANC systems require an error microphone at the user's ear canal to measure residual sound, preventing deployment on emerging open-ear wearable devices such as smart glasses and VR headsets, which leave the ear unoccluded. Here we present an ANC system for open-ear wearables that suppresses environmental noise using only microphones and miniaturized open-ear speakers embedded within the frame of the wearables, removing the need for an in-ear error microphone. Our low-latency computational pipeline uses a neural network to estimate the noise at the ear from an array of eight microphones distributed around the wearable's frame and generates an anti-noise signal in real-time. This mapping generalizes to unseen users and acoustic environments without prior acoustic measurement. We develop a custom glasses prototype and evaluate across eleven unseen users and eight unseen environments under mobility in the 100 to 1000 Hz frequency range, where environmental noise is concentrated. We achieve a mean noise reduction of 9.6 dB without any calibration, and 11.2 dB with a brief user-specific calibration. Further, we demonstrate that our approach extends to the broader class of open-ear wearables including VR headsets and headbands.

eess.AS

VergeIO: Depth-Aware Eye Interaction on Glasses

There is growing industry interest in unobtrusive designs for electrooculography (EOG) sensing of eye gestures on glasses (e.g. JINS MEME and Apple eyewear). We present VergeIO, an EOG-based glasses system that enables depth-aware eye interaction by sensing vergence with a glasses-compatible electrode layout and smart glass prototype. It can distinguish between four depth-based eye gestures with 97% accuracy on unseen users without any calibration in a user study across 20 users and 1,520 gesture instances. To reduce false detections, we incorporate a motion artifact detection pipeline and a preamble-based activation scheme. The system uses dry sensors without any adhesives or gel and operates in real time with 3 mW power consumption by the analog sensing front-end.

cs.HC

SonicSieve: Bringing Directional Speech Extraction to Smartphones Using Acoustic Microstructures

Imagine placing your smartphone on a table in a noisy restaurant and clearly capturing the voices of friends seated around you, or recording a lecturer's voice with clarity in a reverberant auditorium. We introduce SonicSieve, the first intelligent directional speech extraction system for smartphones using a bio-inspired acoustic microstructure. Our passive design embeds directional cues onto incoming speech without any additional electronics. It attaches to the in-line mic of low-cost wired earphones which can be attached to smartphones. We present an end-to-end neural network that processes the raw audio mixtures in real-time on mobile devices. Our results show that SonicSieve achieves a signal quality improvement of 5.0 dB when focusing on a 30{\deg} angular region. Additionally, the performance of our system based on only two microphones exceeds that of conventional 5-microphone arrays.

cs.SD