Sound Event Detection Based on Beamformed Convolutional Neural Network Using Multi-Microphones
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Jaehun | - |
dc.contributor.author | Noh, Kyoungin | - |
dc.contributor.author | Kim, Jaeha | - |
dc.contributor.author | Chang, Joon Hyuk | - |
dc.date.accessioned | 2021-07-30T05:31:31Z | - |
dc.date.available | 2021-07-30T05:31:31Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2018-11 | - |
dc.identifier.issn | 2374-0272 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/5247 | - |
dc.description.abstract | This paper presents a real environment sound event detection method based on pre-processing technology. Our goal is to improve the performance of the sound event detection using a pre-processing module called parameterized multi-channel non-causal Wiener filter (PMWF). First, we convert the existing 1 channel data to 2 channels through the Room impulse response generator (RIR) module. The reason for 2-channel conversion is that PMWF requires multiple channels for beamforming. Noise cancellation is performed through PMWF and the results are derived through the proposed convolutional neural network model. As a result, we found that this method has a good effect on real-time sound event detection, and we found that peak normalization and median filter also have a good effect. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Sound Event Detection Based on Beamformed Convolutional Neural Network Using Multi-Microphones | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chang, Joon Hyuk | - |
dc.identifier.doi | 10.1109/ICNIDC.2018.8525597 | - |
dc.identifier.scopusid | 2-s2.0-85058325172 | - |
dc.identifier.bibliographicCitation | Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018, pp.170 - 173 | - |
dc.relation.isPartOf | Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 | - |
dc.citation.title | Proceedings of 2018 6th IEEE International Conference on Network Infrastructure and Digital Content, IC-NIDC 2018 | - |
dc.citation.startPage | 170 | - |
dc.citation.endPage | 173 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Convolution | - |
dc.subject.keywordPlus | Deep neural networks | - |
dc.subject.keywordPlus | Digital integrated circuits | - |
dc.subject.keywordPlus | Impulse response | - |
dc.subject.keywordPlus | Neural networks | - |
dc.subject.keywordPlus | Causal Wiener filter | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Multichannel Wiener filter | - |
dc.subject.keywordPlus | Noise cancellation | - |
dc.subject.keywordPlus | Pre-processing technology | - |
dc.subject.keywordPlus | Real time sound events | - |
dc.subject.keywordPlus | Room impulse response | - |
dc.subject.keywordPlus | Sound event detection | - |
dc.subject.keywordPlus | Median filters | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Deep neural network | - |
dc.subject.keywordAuthor | Median filter | - |
dc.subject.keywordAuthor | Parametric multi-channel Wiener filter | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/8525597 | - |
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