Technical Program

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AUD-P7: Audio Classification

Session Type: Poster
Time: Wednesday, 6 May, 11:30 - 13:30
Location: On-Demand
Virtual Session: View on Virtual Platform
Session Chair: Zafar Rafii, Gracenote
 
 AUD-P7.1: BEYOND THE DCASE 2017 CHALLENGE ON RARE SOUND EVENT DETECTION: A PROPOSAL FOR A MORE REALISTIC TRAINING AND TEST FRAMEWORK
         Jan Baumann; Technische Universität Braunschweig
         Timo Lohrenz; Technische Universität Braunschweig
         Alexander Roy; IAV GmbH
         Tim Fingscheidt; Technische Universität Braunschweig
 
 AUD-P7.2: METRIC LEARNING WITH BACKGROUND NOISE CLASS FOR FEW-SHOT DETECTION OF RARE SOUND EVENTS
         Kazuki Shimada; Sony Corporation
         Yuichiro Koyama; Sony Corporation
         Akira Inoue; Sony Corporation
 
 AUD-P7.3: SOUND EVENT DETECTION BY MULTITASK LEARNING OF SOUND EVENTS AND SCENES WITH SOFT SCENE LABELS
         Keisuke Imoto; Ritsumeikan University
         Noriyuki Tonami; Ritsumeikan University
         Yuma Koizumi; Nippon Telegraph and Telephone Corporation
         Masahiro Yasuda; Nippon Telegraph and Telephone Corporation
         Ryosuke Yamanishi; Ritsumeikan University
         Yoichi Yamashita; Ritsumeikan University
 
 AUD-P7.4: GUIDED LEARNING FOR WEAKLY-LABELED SEMI-SUPERVISED SOUND EVENT DETECTION
         Liwei Lin; Institute of Computing Technology, Chinese Academy of Sciences
         Xiangdong Wang; Institute of Computing Technology, Chinese Academy of Sciences
         Hong Liu; Institute of Computing Technology, Chinese Academy of Sciences
         Yueliang Qian; Institute of Computing Technology, Chinese Academy of Sciences
 
 AUD-P7.5: STAGED TRAINING STRATEGY AND MULTI-ACTIVATION FOR AUDIO TAGGING WITH NOISY AND SPARSE MULTI-LABEL DATA
         Kexin He; Tsinghua University
         Yuhan Shen; Northeastern University
         Wei-Qiang Zhang; Tsinghua University
         Jia Liu; Tsinghua University
 
 AUD-P7.6: LEARNING WITH OUT-OF-DISTRIBUTION DATA FOR AUDIO CLASSIFICATION
         Turab Iqbal; University of Surrey
         Yin Cao; University of Surrey
         Qiuqiang Kong; University of Surrey
         Mark D. Plumbley; University of Surrey
         Wenwu Wang; University of Surrey
 
 AUD-P7.7: MULTI-BRANCH LEARNING FOR WEAKLY-LABELED SOUND EVENT DETECTION
         Yuxin Huang; Institute of Computing Technology, Chinese Academy of Sciences
         Xiangdong Wang; Institute of Computing Technology, Chinese Academy of Sciences
         Liwei Lin; Institute of Computing Technology, Chinese Academy of Sciences
         Hong Liu; Institute of Computing Technology, Chinese Academy of Sciences
         Yueliang Qian; Institute of Computing Technology, Chinese Academy of Sciences
 
 AUD-P7.8: SCENE-DEPENDENT ACOUSTIC EVENT DETECTION WITH SCENE CONDITIONING AND FAKE-SCENE-CONDITIONED LOSS
         Tatsuya Komatsu; LINE Corporation
         Keisuke Imoto; Ritsumeikan University
         Masahito Togami; LINE Corporation
 
 AUD-P7.9: SOUND EVENT LOCALIZATION BASED ON SOUND INTENSITY VECTOR REFINED BY DNN-BASED DENOISING AND SOURCE SEPARATION
         Masahiro Yasuda; NTT Corporation
         Yuma Koizumi; NTT Corporation
         Shoichiro Saito; NTT Corporation
         Hisashi Uematsu; NTT Corporation
         Keisuke Imoto; Ritsumeikan University
 
 AUD-P7.10: HIGH-RESOLUTION ATTENTION NETWORK WITH ACOUSTIC SEGMENT MODEL FOR ACOUSTIC SCENE CLASSIFICATION
         Xue Bai; University of Science and Technology of China
         Jun Du; University of Science and Technology of China
         Jia Pan; University of Science and Technology of China
         Heng-Shun Zhou; University of Science and Technology of China
         Yan-Hui Tu; University of Science and Technology of China
         Chin-Hui Lee; Georgia Institute of Technology
 
 AUD-P7.11: POLYPHONIC SOUND EVENT DETECTION USING TRANSPOSED CONVOLUTIONAL RECURRENT NEURAL NETWORK
         Chandra Churh Chatterjee; Jalpaiguri Government Engineering College
         Manjunath Mulimani; Manipal Institute of Technology Manipal
         Shashidhar G Koolagudi; National Institute of Technology Karnataka
 
 AUD-P7.12: SECOST: SEQUENTIAL CO-SUPERVISION FOR LARGE SCALE WEAKLY LABELED AUDIO EVENT DETECTION
         Anurag Kumar; Facebook Inc
         Vamsi Krishna Ithapu; Facebook