MaskClip: Detachable Clip-on Piezoelectric Sensing of Mask Surface Vibrations for Real-time Noise-Robust Speech Input
📝 Original Info
- Title: MaskClip: Detachable Clip-on Piezoelectric Sensing of Mask Surface Vibrations for Real-time Noise-Robust Speech Input
- ArXiv ID: 2505.02180
- Date: 2025-05-04
- Authors: Hirotaka Hiraki, Jun Rekimoto
📝 Abstract
Masks are essential in medical settings and during infectious outbreaks but significantly impair speech communication, especially in environments with background noise. Existing solutions often require substantial computational resources or compromise hygiene and comfort. We propose a novel sensing approach that captures only the wearer's voice by detecting mask surface vibrations using a piezoelectric sensor. Our developed device, MaskClip, employs a stainless steel clip with an optimally positioned piezoelectric sensor to selectively capture speech vibrations while inherently filtering out ambient noise. Evaluation experiments demonstrated superior performance with a low Character Error Rate of 6.1\% in noisy environments compared to conventional microphones. Subjective evaluations by 102 participants also showed high satisfaction scores. This approach shows promise for applications in settings where clear voice communication must be maintained while wearing protective equipment, such as medical facilities, cleanrooms, and industrial environments.💡 Deep Analysis
Deep Dive into MaskClip: Detachable Clip-on Piezoelectric Sensing of Mask Surface Vibrations for Real-time Noise-Robust Speech Input.Masks are essential in medical settings and during infectious outbreaks but significantly impair speech communication, especially in environments with background noise. Existing solutions often require substantial computational resources or compromise hygiene and comfort. We propose a novel sensing approach that captures only the wearer’s voice by detecting mask surface vibrations using a piezoelectric sensor. Our developed device, MaskClip, employs a stainless steel clip with an optimally positioned piezoelectric sensor to selectively capture speech vibrations while inherently filtering out ambient noise. Evaluation experiments demonstrated superior performance with a low Character Error Rate of 6.1% in noisy environments compared to conventional microphones. Subjective evaluations by 102 participants also showed high satisfaction scores. This approach shows promise for applications in settings where clear voice communication must be maintained while wearing protective equipment, suc