Bone Conduction Microphone

Bone conduction microphones (BCMs) offer a promising alternative to traditional air-conduction microphones, particularly in noisy environments, by sensing vibrations transmitted through bone. Current research focuses on developing efficient algorithms, often employing lightweight neural network architectures like transformers and recurrent neural networks, to enhance the often-limited bandwidth and quality of BCM signals, including tasks like voice activity detection (VAD) and audio super-resolution. These advancements aim to overcome challenges such as limited data availability and computational constraints on resource-limited devices like hearing aids and earbuds, leading to improved speech clarity and power efficiency in wearable audio applications. The resulting improvements in speech quality and reduced power consumption have significant implications for the design of more effective and user-friendly hearing and communication devices.