  Voice communication using the air conduction microphone in noisy environments
suffers from the degradation of speech audibility. Bone conduction microphones
(BCM) are robust against ambient noises but suffer from limited effective
bandwidth due to their sensing mechanism. Although existing audio super
resolution algorithms can recover the high frequency loss to achieve
high-fidelity audio, they require considerably more computational resources
than available in low-power hearable devices. This paper proposes the
first-ever real-time on-chip speech audio super resolution system for BCM. To
accomplish this, we built and compared a series of lightweight audio super
resolution deep learning models. Among all these models, ATS-UNet is the most
cost-efficient because the proposed novel Audio Temporal Shift Module (ATSM)
reduces the network's dimensionality while maintaining sufficient temporal
features from speech audios. Then we quantized and deployed the ATS-UNet to
low-end ARM micro-controller units for real-time embedded prototypes.
Evaluation results show that our system achieved real-time inference speed on
Cortex-M7 and higher quality than the baseline audio super resolution method.
Finally, we conducted a user study with ten experts and ten amateur listeners
to evaluate our method's effectiveness to human ears. Both groups perceived a
significantly higher speech quality with our method when compared to the
solutions with the original BCM or air conduction microphone with cutting-edge
noise reduction algorithms.
