Deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing. (October 2022) - Jung, Young Hoon; Pham, Trung Xuan; Issa, Dias; Wang, Hee Seung; Lee, Jae Hee; Chung, Mingi; Lee, Bo-Yeon; Kim, Gwangsu; Yoo, Chang D.; Lee, Keon Jae
Abstract: 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 application, 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 (Nb-doped PZT; PNZT) 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 filtered the crowd condition noises, showing independent speaker's speeches can be identified and digitalized simultaneously. Graphical Abstract: To fabricate the noise-robust flexible piezoelectric acoustic sensor (NPAS), the multi-resonant bands are designed outside the noise dominant frequency range, via biomimetic resonance mechanism. Broad voice coverage up to 8 kHz is achieved by adopting an advanced piezoelectric membrane with the material and dimensional effect analysis. 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. ga1 Highlights: We designed the multi-resonant bands outside the noise dominant frequency range. Broad voice coverage up to 8 kHz was achieved by adopting Nb-doped PZT piezoelectric membrane. Deep learning-based speech processing of our sensor showed the outstanding improvement compared to a commercial microphone.