Deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing

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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 multichannel 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.
Publisher
ELSEVIER
Issue Date
2022-10
Language
English
Article Type
Article
Citation

NANO ENERGY, v.101

ISSN
2211-2855
DOI
10.1016/j.nanoen.2022.107610
URI
http://hdl.handle.net/10203/297898
Appears in Collection
EE-Journal Papers(저널논문)MS-Journal Papers(저널논문)
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