AASP-P1.5
ACTIVE LEARNING FOR SOUND EVENT CLASSIFICATION USING BAYESIAN NEURAL NETWORKS WITH GAUSSIAN VARIATIONAL POSTERIOR
Stepan Shishkin, Danilo Hollosi, Fraunhofer Institute for Digital Media Technology IDMT, Germany; Stefan Goetze, The University of Sheffield, United Kingdom of Great Britain and Northern Ireland; Simon Doclo, Carl von Ossietzky Universität Oldenburg, Germany
Session:
AASP-P1: Audio events detection and classification; Music Information Retrieval 1 Poster
Track:
Audio and Acoustic Signal Processing
Location:
Poster Zone 1A
Poster Board PZ-1A.5
Poster Board PZ-1A.5
Presentation Time:
Tue, 16 Apr, 13:10 - 15:10 (UTC +9)
Session Chair:
Dasaem Jeong, Sogang University
Session AASP-P1
AASP-P1.1: SEMI-SUPERVISED SOUND EVENT DETECTION WITH LOCAL AND GLOBAL CONSISTENCY REGULARIZATION
Yiming Li, Xiangdong Wang, Hong Liu, Institute of Computing Technology, Chinese Academy of Sciences, China; Rui Tao, Long Yan, Toshiba(china), China; Kazushige Ouchi, Toshiba (China) Co., Ltd, Japan
AASP-P1.2: ”IT IS OKAY TO BE UNCOMMON”: QUANTIZING SOUND EVENT DETECTION NETWORKS ON HARDWARE ACCELERATORS WITH UNCOMMON SUB-BYTE SUPPORT
Yushu Wu, Northeastern University, United States of America; Xiao Quan, Russell Izadi, Chuan-Che Huang, Bose Corporation, United States of America
AASP-P1.3: FINE-TUNE THE PRETRAINED ATST MODEL FOR SOUND EVENT DETECTION
Nian Shao, Zhejiang University; Westlake University, China; Xian Li, Xiaofei Li, Westlake University; Westlake Institute for Advanced Study, China
AASP-P1.4: A UNIFIED LOSS FUNCTION TO TACKLE INTER-CLASS AND INTRA-CLASS DATA IMBALANCE IN SOUND EVENT DETECTION
Yuliang Zhang, Roberto Togneri, David Huang, The University of Western Australia, Australia
AASP-P1.5: ACTIVE LEARNING FOR SOUND EVENT CLASSIFICATION USING BAYESIAN NEURAL NETWORKS WITH GAUSSIAN VARIATIONAL POSTERIOR
Stepan Shishkin, Danilo Hollosi, Fraunhofer Institute for Digital Media Technology IDMT, Germany; Stefan Goetze, The University of Sheffield, United Kingdom of Great Britain and Northern Ireland; Simon Doclo, Carl von Ossietzky Universität Oldenburg, Germany
AASP-P1.6: SSL-NET: A SYNERGISTIC SPECTRAL AND LEARNING-BASED NETWORK FOR EFFICIENT BIRD SOUND CLASSIFICATION
Yiyuan Yang, Kaichen Zhou, Niki Trigoni, Andrew Markham, University of Oxford, United Kingdom of Great Britain and Northern Ireland
AASP-P1.7: CLASS-INCREMENTAL LEARNING FOR MULTI-LABEL AUDIO CLASSIFICATION
Manjunath Mulimani, Annamaria Mesaros, Tampere University, Finland
AASP-P1.8: AN EXPLAINABLE PROXY MODEL FOR MULTILABEL AUDIO SEGMENTATION
Théo Mariotte, Le Mans Université, France; Antonio Almudévar, University of Zaragoza, Spain; Marie Tahon, Le Mans Université, France; Alfonso Ortega, University of Zaragoza, Spain
AASP-P1.9: A FOUNDATION MODEL FOR MUSIC INFORMATICS
Minz Won, Suno, United States of America; Yun-Ning Hung, Duc Le, ByteDance, United States of America
AASP-P1.10: PIANO TRANSCRIPTION WITH HARMONIC ATTENTION
Ruimin Wu, Xianke Wang, Yuqing Li, Wei Xu, Wenqing Cheng, School of Electronic Information and Communications, Hubei Provincial Key Laboratory of Smart Internet Technology, Huazhong University of Science and Technology, China
AASP-P1.11: TIMBRE-TRAP: A LOW-RESOURCE FRAMEWORK FOR INSTRUMENT-AGNOSTIC MUSIC TRANSCRIPTION
Frank Cwitkowitz, University of Rochester, United States of America; Kin Wai Cheuk, Woosung Choi, Marco Martínez-Ramírez, Keisuke Toyama, Wei-Hsiang Liao, Yuki Mitsufuji, Sony, Japan
AASP-P1.12: TEMPO ESTIMATION AS FULLY SELF-SUPERVISED BINARY CLASSIFICATION
Florian Henkel, Jaehun Kim, Matthew McCallum, Samuel Sandberg, Matthew Davies, SiriusXM-Pandora, United States of America
Contacts