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TU1.I: Deep Learning for Audio Classification

Session Type: Poster
Time: Tuesday, 5 May, 11:30 - 13:30
Location: On-Demand
Session Chair: Nobutaka Ito, NTT
 
   TU1.I.1: ANOMALOUS SOUND DETECTION BASED ON INTERPOLATION DEEP NEURAL NETWORK
         Kaori Suefusa; Hitachi, Ltd.
         Tomoya Nishida; University of Tokyo
         Purohit Harsh; Hitachi, Ltd.
         Ryo Tanabe; Hitachi, Ltd.
         Takashi Endo; Hitachi, Ltd.
         Yohei Kawaguchi; Hitachi, Ltd.
 
   TU1.I.2: A-CRNN: A DOMAIN ADAPTATION MODEL FOR SOUND EVENT DETECTION
         Wei Wei; National University of Singapore
         Hongning Zhu; Fudan University
         Emmanouil Benetos; Queen Mary University of London
         Ye Wang; National University of Singapore
 
   TU1.I.3: SPIDERNET: ATTENTION NETWORK FOR ONE-SHOT ANOMALY DETECTION IN SOUNDS
         Yuma Koizumi; NTT Corporation
         Masahiro Yasuda; NTT Corporation
         Shin Murata; NTT Corporation
         Shoichiro Saito; NTT Corporation
         Hisashi Uematsu; NTT Corporation
         Noboru Harada; NTT Corporation
 
   TU1.I.4: SOUND EVENT DETECTION VIA DILATED CONVOLUTIONAL RECURRENT NEURAL NETWORKS
         Yanxiong Li; South China University of Technology
         Mingle Liu; South China University of Technology
         Konstantinos Drossos; Tampere University
         Tuomas Virtanen; Tampere University
 
   TU1.I.5: A DEEP NEURAL NETWORK-DRIVEN FEATURE LEARNING METHOD FOR POLYPHONIC ACOUSTIC EVENT DETECTION FROM REAL-LIFE RECORDINGS
         Manjunath Mulimani; Manipal Institute of Technology Manipal
         Akash B Kademani; Symbiosis Institute of Technology
         Shashidhar G Koolagudi; National Institute of Technology Karnataka
 
   TU1.I.6: WEAKLY LABELLED AUDIO TAGGING VIA CONVOLUTIONAL NETWORKS WITH SPATIAL AND CHANNEL-WISE ATTENTION
         Sixin Hong; Peking University
         Yuexian Zou; Peking University
         Wenwu Wang; University of Surrey
         Meng Cao; Peking University
 
   TU1.I.7: A STUDY ON THE TRANSFERABILITY OF ADVERSARIAL ATTACKS IN SOUND EVENT CLASSIFICATION
         Vinod Subramanian; Queen Mary University of London
         Arjun Pankajakshan; Queen Mary University of London
         Emmanouil Benetos; Queen Mary University of London
         Ning Xu; ROLI Ltd.
         SKoT McDonald; ROLI Ltd.
         Mark Sandler; Queen Mary University of London
 
   TU1.I.8: PROPELLER NOISE DETECTION WITH DEEP LEARNING
         Thomas Mahiout; Thales
         Lionel Fillatre; UCA
         Laurent Deruaz-Pepin; Thales
 
   TU1.I.9: DURATION ROBUST WEAKLY SUPERVISED SOUND EVENT DETECTION
         Heinrich Dinkel; Shanghai Jiao Tong University
         Kai Yu; Shanghai Jiao Tong University
 
   TU1.I.10: A COMPARISON OF POOLING METHODS ON LSTM MODELS FOR RARE ACOUSTIC EVENT CLASSIFICATION
         Chieh-Chi Kao; Amazon, Inc.
         Ming Sun; Amazon, Inc.
         Weiran Wang; Salesforce Research
         Chao Wang; Amazon, Inc.
 
   TU1.I.11: AN ONTOLOGY-AWARE FRAMEWORK FOR AUDIO EVENT CLASSIFICATION
         Yiwei Sun; Pennsylvania State University
         Shabnam Ghaffarzadegan; Bosch Research and Technology Center
 
   TU1.I.12: TASK-AWARE MEAN TEACHER METHOD FOR LARGE SCALE WEAKLY LABELED SEMI-SUPERVISED SOUND EVENT DETECTION
         Jie Yan; University of Science and Technology of China
         Yan Song; University of Science and Technology of China
         Li-Rong Dai; University of Science and Technology of China
         Ian McLoughlin; University of Kent