DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jung, Young Hoon | ko |
dc.contributor.author | Pham, Trung Xuan | ko |
dc.contributor.author | Issa, Dias | ko |
dc.contributor.author | Wang, Hee Seung | ko |
dc.contributor.author | Lee, Jae Hee | ko |
dc.contributor.author | Chung, Mingi | ko |
dc.contributor.author | Lee, Bo-Yeon | ko |
dc.contributor.author | Kim, Gwangsu | ko |
dc.contributor.author | Yoo, Chang-Dong | ko |
dc.contributor.author | Lee, Keon Jae | ko |
dc.date.accessioned | 2022-08-09T06:00:21Z | - |
dc.date.available | 2022-08-09T06:00:21Z | - |
dc.date.created | 2022-08-09 | - |
dc.date.created | 2022-08-09 | - |
dc.date.created | 2022-08-09 | - |
dc.date.issued | 2022-10 | - |
dc.identifier.citation | NANO ENERGY, v.101 | - |
dc.identifier.issn | 2211-2855 | - |
dc.identifier.uri | http://hdl.handle.net/10203/297898 | - |
dc.description.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 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. | - |
dc.language | English | - |
dc.publisher | ELSEVIER | - |
dc.title | Deep learning-based noise robust flexible piezoelectric acoustic sensors for speech processing | - |
dc.type | Article | - |
dc.identifier.wosid | 000832170700002 | - |
dc.identifier.scopusid | 2-s2.0-85134656852 | - |
dc.type.rims | ART | - |
dc.citation.volume | 101 | - |
dc.citation.publicationname | NANO ENERGY | - |
dc.identifier.doi | 10.1016/j.nanoen.2022.107610 | - |
dc.contributor.localauthor | Yoo, Chang-Dong | - |
dc.contributor.localauthor | Lee, Keon Jae | - |
dc.contributor.nonIdAuthor | Pham, Trung Xuan | - |
dc.contributor.nonIdAuthor | Lee, Bo-Yeon | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Flexible piezoelectric | - |
dc.subject.keywordAuthor | Acoustic sensor | - |
dc.subject.keywordAuthor | Deep learning algorithm | - |
dc.subject.keywordAuthor | Noise -robust speaker recognition | - |
dc.subject.keywordAuthor | Speech enhancement | - |
dc.subject.keywordPlus | LOW-FREQUENCY NOISE | - |
dc.subject.keywordPlus | HUMAN VOICE | - |
dc.subject.keywordPlus | MICROPHONE | - |
dc.subject.keywordPlus | FILMS | - |
dc.subject.keywordPlus | RECOGNITION | - |
dc.subject.keywordPlus | PEOPLE | - |
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