DC Field | Value | Language |
---|---|---|
dc.contributor.author | Han, Jae Hyun | ko |
dc.contributor.author | Bae, Kang Min | ko |
dc.contributor.author | Hong, Seong Kwang | ko |
dc.contributor.author | Park, Hyunsin | ko |
dc.contributor.author | Kwak, Jun-Hyuk | ko |
dc.contributor.author | Wang, Hee Seung | ko |
dc.contributor.author | Joe, Daniel Juhyung | ko |
dc.contributor.author | Park, Jung Hwan | ko |
dc.contributor.author | Jung, Young Hoon | ko |
dc.contributor.author | Hur, Shin | ko |
dc.contributor.author | Yoo, Chang-Dong | ko |
dc.contributor.author | Lee, Keon Jae | ko |
dc.date.accessioned | 2018-11-22T07:07:47Z | - |
dc.date.available | 2018-11-22T07:07:47Z | - |
dc.date.created | 2018-11-19 | - |
dc.date.created | 2018-11-19 | - |
dc.date.issued | 2018-11 | - |
dc.identifier.citation | NANO ENERGY, v.53, pp.658 - 665 | - |
dc.identifier.issn | 2211-2855 | - |
dc.identifier.uri | http://hdl.handle.net/10203/246895 | - |
dc.description.abstract | Herein, we report a new platform of machine learning-based speaker recognition via the flexible piezoelectric acoustic sensor (f-PAS) with a highly sensitive multi-resonant frequency band. The resonant self-powered f-PAS was fabricated by mimicking the operating mechanism of the basilar membrane in the human cochlear. The f-PAS acquired abundant voice information from the multi-channel sound inputs. The standard TIDIGITS dataset were recorded by the f-PAS and converted to frequency components by using a Fast Fourier Transform (FFT) and a Short-Time Fourier Transform (STFT). The machine learning based Gaussian Mixture Model (GMM) was designed by utilizing the most highest and second highest sensitivity data among multi-channel outputs, exhibiting outstanding speaker recognition rate of 97.5% with error rate reduction of 75% compared to that of the reference MEMS microphone. | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.subject | VOICE RECOGNITION | - |
dc.subject | NANOSENSORS | - |
dc.title | Machine learning-based self-powered acoustic sensor for speaker recognition | - |
dc.type | Article | - |
dc.identifier.wosid | 000448994600074 | - |
dc.identifier.scopusid | 2-s2.0-85053804870 | - |
dc.type.rims | ART | - |
dc.citation.volume | 53 | - |
dc.citation.beginningpage | 658 | - |
dc.citation.endingpage | 665 | - |
dc.citation.publicationname | NANO ENERGY | - |
dc.identifier.doi | 10.1016/j.nanoen.2018.09.030 | - |
dc.contributor.localauthor | Joe, Daniel Juhyung | - |
dc.contributor.localauthor | Yoo, Chang-Dong | - |
dc.contributor.localauthor | Lee, Keon Jae | - |
dc.contributor.nonIdAuthor | Bae, Kang Min | - |
dc.contributor.nonIdAuthor | Hong, Seong Kwang | - |
dc.contributor.nonIdAuthor | Kwak, Jun-Hyuk | - |
dc.contributor.nonIdAuthor | Jung, Young Hoon | - |
dc.contributor.nonIdAuthor | Hur, Shin | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | Flexible piezoelectric | - |
dc.subject.keywordAuthor | Self-powered | - |
dc.subject.keywordAuthor | Acoustic sensor | - |
dc.subject.keywordAuthor | Machine learning algorithm | - |
dc.subject.keywordAuthor | Speaker recognition | - |
dc.subject.keywordPlus | VOICE RECOGNITION | - |
dc.subject.keywordPlus | NANOSENSORS | - |
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