AUD-L2: Deep Learning for Source Separation |
Session Type: Lecture |
Time: Tuesday, 5 May, 16:30 - 18:30 |
Location: On-Demand |
Virtual Session: View on Virtual Platform |
Session Chair: Minje Kim, Indiana University Bloomington |
AUD-L2.1: TWO-STEP SOUND SOURCE SEPARATION: TRAINING ON LEARNED LATENT TARGETS |
Efthymios Tzinis; University of Illinois at Urbana-Champaign |
Shrikant Venkataramani; University of Illinois at Urbana-Champaign |
Zhepei Wang; University of Illinois at Urbana-Champaign |
Cem Subakan; Mila-Quebec Artificial Intelligence Institute |
Paris Smaragdis; University of Illinois at Urbana-Champaign, Adobe Research |
AUD-L2.2: A MULTI-PHASE GAMMATONE FILTERBANK FOR SPEECH SEPARATION VIA TASNET |
David Ditter; Universität Hamburg |
Timo Gerkmann; Universität Hamburg |
AUD-L2.3: IMPROVING VOICE SEPARATION BY INCORPORATING END-TO-END SPEECH RECOGNITION |
Naoya Takahashi; Sony Corporation |
Mayank Singh; Indian Institute of Technology Bombay |
Sakya Basak; Indian Institute of Science |
Parthasaarathy Sudarsanam; Sony India Software Centre |
Sriram Ganapathy; Indian Institute of Science |
Yuki Mitsufuji; Sony Corporation |
AUD-L2.4: DUAL-PATH RNN: EFFICIENT LONG SEQUENCE MODELING FOR TIME-DOMAIN SINGLE-CHANNEL SPEECH SEPARATION |
Yi Luo; Columbia University |
Zhuo Chen; Microsoft |
Takuya Yoshioka; Microsoft |
AUD-L2.5: CONTROLLING THE PERCEIVED SOUND QUALITY FOR DIALOGUE ENHANCEMENT WITH DEEP LEARNING |
Christian Uhle; Fraunhofer Institute for Integrated Circuits IIS |
Matteo Torcoli; Fraunhofer Institute for Integrated Circuits IIS |
Jouni Paulus; Fraunhofer Institute for Integrated Circuits IIS |
AUD-L2.6: UNSUPERVISED TRAINING FOR DEEP SPEECH SOURCE SEPARATION WITH KULLBACK-LEIBLER DIVERGENCE BASED PROBABILISTIC LOSS FUNCTION |
Masahito Togami; LINE Corporation |
Yoshiki Masuyama; Waseda University |
Tatsuya Komatsu; LINE Corporation |
Yu Nakagome; Waseda University |