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WE2.I: Audio and Speech Source Separation

Session Type: Lecture
Time: Wednesday, 6 May, 11:30 - 13:30
Location: On-Demand
Session Chair: Scott Wisdom, Google
 
   WE2.I.1: LEARNING TO SEPARATE SOUNDS FROM WEAKLY LABELED SCENES
         Fatemeh Pishdadian; Northwestern University
         Gordon Wichern; Mitsubishi Electric Research Laboratories (MERL)
         Jonathan Le Roux; Mitsubishi Electric Research Laboratories (MERL)
 
   WE2.I.2: IMPROVING UNIVERSAL SOUND SEPARATION USING SOUND CLASSIFICATION
         Efthymios Tzinis; University of Illinois at Urbana-Champaign
         Scott Wisdom; Google
         John R. Hershey; Google
         Aren Jansen; Google
         Daniel P. W. Ellis; Google Research
 
   WE2.I.3: SOURCE SEPARATION WITH WEAKLY LABELLED DATA: AN APPROACH TO COMPUTATIONAL AUDITORY SCENE ANALYSIS
         Qiuqiang Kong; ByteDance
         Yuxuan Wang; ByteDance
         Xuchen Song; ByteDance
         Yin Cao; University of Surrey
         Wenwu Wang; University of Surrey
         Mark D. Plumbley; University of Surrey
 
   WE2.I.4: BOOSTED LOCALITY SENSITIVE HASHING: DISCRIMINATIVE BINARY CODES FOR SOURCE SEPARATION
         Sunwoo Kim; Indiana University Bloomington
         Haici Yang; Indiana University Bloomington
         Minje Kim; Indiana University Bloomington
 
   WE2.I.5: A FREQUENCY-DOMAIN BSS METHOD BASED ON L1 NORM, UNITARY CONSTRAINT, AND CAYLEY TRANSFORM
         Satoru Emura; NTT
         Hiroshi Sawada; NTT
         Shoko Araki; NTT
         Noboru Harada; NTT
 
   WE2.I.6: END-TO-END NON-NEGATIVE AUTOENCODERS FOR SOUND SOURCE SEPARATION
         Shrikant Venkataramani; University of Illinois at Urbana-Champaign
         Efthymios Tzinis; University of Illinois at Urbana-Champaign
         Paris Smaragdis; University of Illinois at Urbana-Champaign, Adobe Research