The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
약하게 레이블이 지정된 AT(반 감독 오디오 태깅) 및 SED(사운드 이벤트 감지)는 실제 애플리케이션에서 중요해졌습니다. 널리 사용되는 방법은 교사-학생 학습으로, 학생 모델이 레이블이 지정되지 않은 데이터에서 교사 모델이 생성한 의사 레이블을 통해 학습하도록 만드는 것입니다. 고품질 의사 라벨을 생성하기 위해 우리는 이중 리드 정책으로 훈련된 석사-교사-학생 프레임워크를 제안합니다. 우리의 실험은 우리 모델이 두 작업 모두에서 최첨단 모델보다 성능이 우수하다는 것을 보여줍니다.
Yuzhuo LIU
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Hangting CHEN
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Qingwei ZHAO
Chinese Academy of Sciences,University of Chinese Academy of Sciences
Pengyuan ZHANG
Chinese Academy of Sciences,University of Chinese Academy of Sciences
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Yuzhuo LIU, Hangting CHEN, Qingwei ZHAO, Pengyuan ZHANG, "Master-Teacher-Student: A Weakly Labelled Semi-Supervised Framework for Audio Tagging and Sound Event Detection" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 4, pp. 828-831, April 2022, doi: 10.1587/transinf.2021EDL8082.
Abstract: Weakly labelled semi-supervised audio tagging (AT) and sound event detection (SED) have become significant in real-world applications. A popular method is teacher-student learning, making student models learn from pseudo-labels generated by teacher models from unlabelled data. To generate high-quality pseudo-labels, we propose a master-teacher-student framework trained with a dual-lead policy. Our experiments illustrate that our model outperforms the state-of-the-art model on both tasks.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8082/_p
부
@ARTICLE{e105-d_4_828,
author={Yuzhuo LIU, Hangting CHEN, Qingwei ZHAO, Pengyuan ZHANG, },
journal={IEICE TRANSACTIONS on Information},
title={Master-Teacher-Student: A Weakly Labelled Semi-Supervised Framework for Audio Tagging and Sound Event Detection},
year={2022},
volume={E105-D},
number={4},
pages={828-831},
abstract={Weakly labelled semi-supervised audio tagging (AT) and sound event detection (SED) have become significant in real-world applications. A popular method is teacher-student learning, making student models learn from pseudo-labels generated by teacher models from unlabelled data. To generate high-quality pseudo-labels, we propose a master-teacher-student framework trained with a dual-lead policy. Our experiments illustrate that our model outperforms the state-of-the-art model on both tasks.},
keywords={},
doi={10.1587/transinf.2021EDL8082},
ISSN={1745-1361},
month={April},}
부
TY - JOUR
TI - Master-Teacher-Student: A Weakly Labelled Semi-Supervised Framework for Audio Tagging and Sound Event Detection
T2 - IEICE TRANSACTIONS on Information
SP - 828
EP - 831
AU - Yuzhuo LIU
AU - Hangting CHEN
AU - Qingwei ZHAO
AU - Pengyuan ZHANG
PY - 2022
DO - 10.1587/transinf.2021EDL8082
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 4
JA - IEICE TRANSACTIONS on Information
Y1 - April 2022
AB - Weakly labelled semi-supervised audio tagging (AT) and sound event detection (SED) have become significant in real-world applications. A popular method is teacher-student learning, making student models learn from pseudo-labels generated by teacher models from unlabelled data. To generate high-quality pseudo-labels, we propose a master-teacher-student framework trained with a dual-lead policy. Our experiments illustrate that our model outperforms the state-of-the-art model on both tasks.
ER -