Technical Program
AASP-L3: Deep Learning for Source Separation and Enhancement I |
Session Type: Lecture |
Time: Wednesday, March 8, 08:30 - 10:30 |
Location: Grand Salon 15 |
Session Chairs: Paris Smaragdis, and Minje Kim, University of Indiana |
AASP-L3.1: DEEP CLUSTERING AND CONVENTIONAL NETWORKS FOR MUSIC SEPARATION: STRONGER TOGETHER |
Yi Luo; Columbia University |
Zhuo Chen; Columbia University |
John R. Hershey; Mitsubishi Electric Research Laboratories |
Jonathan Le Roux; Mitsubishi Electric Research Laboratories |
Nima Mesgarani; Columbia University |
AASP-L3.2: DNN-BASED SPEECH MASK ESTIMATION FOR EIGENVECTOR BEAMFORMING |
Lukas Pfeifenberger; TU Graz |
Matthias Zöhrer; TU Graz |
Franz Pernkopf; TU Graz |
AASP-L3.3: RECURRENT DEEP STACKING NETWORKS FOR SUPERVISED SPEECH SEPARATION |
Zhong-Qiu Wang; The Ohio State University |
DeLiang Wang; The Ohio State University |
AASP-L3.4: COLLABORATIVE DEEP LEARNING FOR SPEECH ENHANCEMENT: A RUN-TIME MODEL SELECTION METHOD USING AUTOENCODERS |
Minje Kim; Indiana University Bloomington |
AASP-L3.5: DNN-BASED SOURCE ENHANCEMENT SELF-OPTIMIZED BY REINFORCEMENT LEARNING USING SOUND QUALITY MEASUREMENTS |
Yuma Koizumi; NTT Corporation |
Kenta Niwa; NTT Corporation |
Yusuke Hioka; University of Auckland |
Kazunori Kobayashi; NTT Corporation |
Yoichi Haneda; The University of Electro-Communications |
AASP-L3.6: A NEURAL NETWORK ALTERNATIVE TO NON-NEGATIVE AUDIO MODELS |
Paris Smaragdis; University of Illinois |
Shrikant Venkataramani; University of Illinois |