ASMSP-SS2.1
HOW MUCH DOES MACHINE IDENTITY MATTER IN ANOMALOUS SOUND DETECTION AT TEST TIME?
Kevin Wilkinghoff, Aalborg University/Pioneer Centre for AI, Denmark; Keisuke Imoto, Kyoto University, Japan; Zheng-Hua Tan, Aalborg University/Pioneer Centre for AI, Denmark
Session:
ASMSP-SS2: Sound of Change – Advances in Acoustic Anomaly Detection Lecture
Track:
Special Sessions
Location:
Forum 6
Presentation Time:
Thu, 3 Sep, 10:30 - 10:50 Belgium Time (UTC +2)
Presentation
Discussion
Resources
No resources available.
Session ASMSP-SS2
ASMSP-SS2.1: HOW MUCH DOES MACHINE IDENTITY MATTER IN ANOMALOUS SOUND DETECTION AT TEST TIME?
Kevin Wilkinghoff, Aalborg University/Pioneer Centre for AI, Denmark; Keisuke Imoto, Kyoto University, Japan; Zheng-Hua Tan, Aalborg University/Pioneer Centre for AI, Denmark
ASMSP-SS2.2: MEMNMF: MEMORY-AUGMENTED NMF ON LPC SPECTRA FOR ANOMALOUS SOUND DETECTION
Phurich Saengthong, Takahiro Shinozaki, Institute of Science Tokyo, Japan
ASMSP-SS2.3: SPIKE BASED APPROACH FOR ANOMALY LOCALIZATION IN LENGTHY BIOACOUSTIC RECORDINGS
Sarangan Raveendranathan, Prasanga Samarasinghe, Thushara Abhayapala, The Australian National University, Australia
ASMSP-SS2.4: Generalized Zero-Shot Learning of Acoustic Event Using Attribute-Conditioned GAN and Out-of-Distribution Detector
Kohei Uehara, Kobe University, Japan; Ryoichi Takashima, Ritsumeikan University, Japan; Tetsuya Takiguchi, Kobe University, Japan
ASMSP-SS2.5: Inter-Audio Diverse Decoding for Audio Difference Captioning in Unsupervised Anomalous Sound Detection Scenarios
Tomoya Nishida, Yusuke Kanamori, Harsh Purohit, Kota Dohi, Takashi Endo, Yohei Kawaguchi, Hitachi, ltd., Japan