AASP-P10.5
SELF-SUPERVISED LEARNING FOR ANOMALOUS SOUND DETECTION
Kevin Wilkinghoff, Fraunhofer FKIE, Germany
Session:
AASP-P10: Anomaly detection; Sound event detection and localization Poster
Track:
Audio and Acoustic Signal Processing
Location:
Poster Zone 6A
Poster Board PZ-6A.5
Poster Board PZ-6A.5
Presentation Time:
Wed, 17 Apr, 16:30 - 18:30 (UTC +9)
Session Chair:
Keisuke Imoto, Doshisha University
Session AASP-P10
AASP-P10.1: FIRST-SHOT UNSUPERVISED ANOMALOUS SOUND DETECTION WITH UNKNOWN ANOMALIES ESTIMATED BY METADATA-ASSISTED AUDIO GENERATION
Hejing Zhang, Harbin Engineering University, China; Qiaoxi Zhu, University of Technology Sydney, Australia; Jian Guan, Harbin Engineering University, China; Haohe Liu, University of Surrey, United Kingdom of Great Britain and Northern Ireland; Feiyang Xiao, Jiantong Tian, Harbin Engineering University, China; Xinhao Mei, Xubo Liu, Wenwu Wang, University of Surrey, United Kingdom of Great Britain and Northern Ireland
AASP-P10.2: Exploring large scale pre-trained models for robust machine anomalous sound detection
Bing Han, Shanghai Jiao Tong University, China; Zhiqiang Lv, Huakong AI Plus Company Limited, China; Anbai Jiang, Tsinghua University, China; Wen Huang, Zhengyang Chen, Shanghai Jiao Tong University, China; Yufeng Deng, Jiawei Ding, Huakong AI Plus Company Limited, China; Cheng Lu, North China Electric Power University, China; Wei-Qiang Zhang, Pingyi Fan, Jia Liu, Tsinghua University, China; Yanmin Qian, Shanghai Jiao Tong University, China
AASP-P10.3: FEW-SHOT ANOMALOUS SOUND DETECTION BASED ON ANOMALY MAP ESTIMATION USING PSEUDO ABNORMAL DATA
Ryosuke Tanaka, Satoshi Tamura, Gifu University, Japan
AASP-P10.4: DP-MAE: A DUAL-PATH MASKED AUTOENCODER BASED SELF-SUPERVISED LEARNING METHOD FOR ANOMALOUS SOUND DETECTION
Zhuo-li Liu, Yan Song, Xiao-Min Zeng, Li-Rong Dai, University of Science and Technology of China, China; Ian McLoughlin, ICT Cluster, Singapore Institute of Technology, Singapore, United Kingdom of Great Britain and Northern Ireland
AASP-P10.5: SELF-SUPERVISED LEARNING FOR ANOMALOUS SOUND DETECTION
Kevin Wilkinghoff, Fraunhofer FKIE, Germany
AASP-P10.6: NOISY-ARCMIX: ADDITIVE NOISY ANGULAR MARGIN LOSS COMBINED WITH MIXUP FOR ANOMALOUS SOUND DETECTION
Soonhyeon Choi, Jung-Woo Choi, Korea Advanced Institute of Science and Technology (KAIST), Korea, Republic of
AASP-P10.7: FINE-GRAINED ENGINE FAULT SOUND EVENT DETECTION USING MULTIMODAL SIGNALS
Dennis Fedorishin, University at Buffalo, United States of America; Livio Forte III, Philip Schneider, ACV Auctions, United States of America; Srirangaraj Setlur, Venu Govindaraju, University at Buffalo, United States of America
AASP-P10.8: A DUAL-PATH FRAMEWORK WITH FREQUENCY-AND-TIME EXCITED NETWORK FOR ANOMALOUS SOUND DETECTION
Yucong Zhang, Juan Liu, Wuhan University, China; Yao Tian, OPPO, China; Haifeng Liu, University of Science and Technology of China, China; Ming Li, Duke Kunshan University, China
AASP-P10.9: WHY DO ANGULAR MARGIN LOSSES WORK WELL FOR SEMI-SUPERVISED ANOMALOUS SOUND DETECTION?
Kevin Wilkinghoff, Frank Kurth, Fraunhofer FKIE, Germany
AASP-P10.10: ZERO- AND FEW-SHOT SOUND EVENT LOCALIZATION AND DETECTION
Kazuki Shimada, Kengo Uchida, Sony AI, Japan; Yuichiro Koyama, Sony Group Corporation, Japan; Takashi Shibuya, Sony AI, Japan; Shusuke Takahashi, Sony Group Corporation, Japan; Yuki Mitsufuji, Sony AI, Japan; Tatsuya Kawahara, Kyoto University, Japan
AASP-P10.11: SPATIAL SCAPER: A LIBRARY TO SIMULATE AND AUGMENT SOUNDSCAPES FOR SOUND EVENT LOCALIZATION AND DETECTION IN REALISTIC ROOMS
Iran Roman, Christopher Ick, Siwen Ding, New York University, United States of America; Adrian Roman, University of Southern California, United States of America; Brian McFee, Juan Bello, New York University, United States of America
AASP-P10.12: EXPLORING SELF-SUPERVISED CONTRASTIVE LEARNING OF SPATIAL SOUND EVENT REPRESENTATION
Xilin Jiang, Cong Han, Yinghao Aaron Li, Nima Mesgarani, Columbia University, United States of America
Contacts