Deep Learning-based Noise-Robust Flexible Piezoelectric Acoustic Sensors for Speech Processing

Flexible piezoelectric acoustic sensors (f-PAS) have attracted significant attention as a promising component for voice user interfaces (VUI) in the era of artificial intelligence of things (AIoT). The signal distortion issue of highly sensitive biomimetic f-PAS is one of the most challenging obstacle for real-life applications, due to the fundamental difference compared with the conventional microphones. Here, a noise-robust flexible piezoelectric acoustic sensor (NPAS) is demonstrated by designing the multi-resonant bands outside the noise dominant frequency range. Broad voice coverage up to 8 kHz is achieved by adopting an advanced piezoelectric membrane with the optimized polymer ratio. Deep learning-based speech processing of multi-channel NPAS is demonstrated to show the outstanding improvement in speaker recognition and speech enhancement compared to a commercial microphone. Finally, the NPAS independently identified the multi-user voices in a crowd condition, showing simultaneous speaker separation.