AUD-11.4
A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder
Yang Xiang, Aalborg University & Capturi A/S, Denmark; Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Capturi A/S, Denmark; Mads Græsbøll Christensen, Aalborg University, Denmark
Session:
Deep Learning-Based Single-Channel Speech Enhancement
Track:
Audio and Acoustic Signal Processing
Location:
Gather Area K
Presentation Time:
Mon, 9 May, 22:00 - 22:45 China Time (UTC +8)
Mon, 9 May, 14:00 - 14:45 UTC
Mon, 9 May, 14:00 - 14:45 UTC
Session Chair:
Minje Kim, Indiana University
Session AUD-11
AUD-11.1: BLOOM-NET: BLOCKWISE OPTIMIZATION FOR MASKING NETWORKS TOWARD SCALABLE AND EFFICIENT SPEECH ENHANCEMENT
Sunwoo Kim, Minje Kim, Indiana University, United States of America
AUD-11.2: HGCN: HARMONIC GATED COMPENSATION NETWORK FOR SPEECH ENHANCEMENT
Tianrui Wang, Weibin Zhu, Beijing Jiaotong University, China; Yingying Gao, Junlan Feng, Shilei Zhang, China Mobile Research Institute, China
AUD-11.3: Speech enhancement with neural homomorphic synthesis
Wenbin Jiang, Zhijun Liu, Kai Yu, Fei Wen, Shanghai Jiao Tong University, China
AUD-11.4: A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder
Yang Xiang, Aalborg University & Capturi A/S, Denmark; Jesper Lisby Højvang, Morten Højfeldt Rasmussen, Capturi A/S, Denmark; Mads Græsbøll Christensen, Aalborg University, Denmark
AUD-11.5: INTEGRATING STATISTICAL UNCERTAINTY INTO NEURAL NETWORK-BASED SPEECH ENHANCEMENT
Huajian Fang, Tal Peer, Stefan Wermter, Timo Gerkmann, Universität Hamburg, Germany
AUD-11.6: UNSUPERVISED SPEECH ENHANCEMENT WITH SPEECH RECOGNITION EMBEDDING AND DISENTANGLEMENT LOSSES
Viet Anh Trinh, The City University of New York, United States of America; Sebastian Braun, Microsoft, United States of America