AASP-P9.2
Permutation-alignment method using manifold optimization for frequency-domain blind source separation
Satoru Emura, Kyoto university of advanced science, Japan
Session:
AASP-P9: Source separation 2; Music analysis 2 Poster
Track:
Audio and Acoustic Signal Processing
Location:
Poster Zone 4A
Poster Board PZ-4A.2
Poster Board PZ-4A.2
Presentation Time:
Wed, 17 Apr, 16:30 - 18:30 (UTC +9)
Session Chair:
Gael Richard, Télécom Paris
Session AASP-P9
AASP-P9.1: DETERMINED BSS BY COMBINATION OF IVA AND DNN VIA PROXIMAL AVERAGE
Kazuki Matsumoto, Waseda University, Japan; Kohei Yatabe, Tokyo University of Agriculture and Technology, Japan
AASP-P9.2: Permutation-alignment method using manifold optimization for frequency-domain blind source separation
Satoru Emura, Kyoto university of advanced science, Japan
AASP-P9.3: HYPERBOLIC DISTANCE-BASED SPEECH SEPARATION
Darius Petermann, Indiana University, United States of America; Minje Kim, University of Illinois at Urbana-Champaign, United States of America
AASP-P9.4: Binaural Angular Separation Network
Yang Yang, George Sung, Shao-Fu Shih, Hakan Erdogan, Chehung Lee, Matthias Grundmann, Google, United States of America
AASP-P9.5: JOINT SEPARATION AND LOCALIZATION OF MOVING SOUND SOURCES BASED ON NEURAL FULL-RANK SPATIAL COVARIANCE ANALYSIS
Hokuto Munakata, National Institute of Advanced Industrial Science and Technology (AIST) / Osaka University, Japan; Yoshiaki Bando, National Institute of Advanced Industrial Science and Technology (AIST), Japan; Ryu Takeda, Kazunori Komatani, Osaka University, Japan; Masaki Onishi, National Institute of Advanced Industrial Science and Technology (AIST), Japan
AASP-P9.6: StemGen: A music generation model that listens
Julian Parker, Janne Spijkervet, Katerina Kosta, Furkan Yesiler, Boris Kuznetsov, Ju-Chiang Wang, Matt Avent, Jitong Chen, Duc Le, ByteDance, United Kingdom of Great Britain and Northern Ireland
AASP-P9.7: An Experimental Comparison Of Multi-view Self-supervised Methods For Music Tagging
Gabriel Meseguer-Brocal, Dorian Desblancs, Romain Hennequin, Deezer, France
AASP-P9.8: AINUR: HARMONIZING SPEED AND QUALITY IN DEEP MUSIC GENERATION THROUGH LYRICS-AUDIO EMBEDDINGS
Giuseppe Concialdi, University of Illinois at Chicago & Politecnico di Torino, United States of America; Alkis Koudounas, Eliana Pastor, Politecnico di Torino, Italy; Barbara Di Eugenio, University of Illinois at Chicago, United States of America; Elena Baralis, Politecnico di Torino, Italy
AASP-P9.9: PARODY DETECTION USING SOURCE-TARGET ATTENTION WITH TEACHER-FOURCED LYRICS
Tomoki Ariga, Yosuke Higuchi, Waseda University, Japan; Kazutoshi Hayasaka, Naoki Okamoto, DAIICHIKOSHO CO., LTD., Japan; Tetsuji Ogawa, Waseda University, Japan
AASP-P9.10: BASS ACCOMPANIMENT GENERATION VIA LATENT DIFFUSION
Marco Pasini, Maarten Grachten, Stefan Lattner, Sony Computer Science Laboratories Paris, France
AASP-P9.11: UNSUPERVISED HARMONIC PARAMETER ESTIMATION USING DIFFERENTIABLE DSP AND SPECTRAL OPTIMAL TRANSPORT
Bernardo Torres, Geoffroy Peeters, Gaël Richard, Telecom Paris, France
AASP-P9.12: A fully differentiable model for unsupervised singing voice separation
Gaël Richard, Pierre Chouteau, Bernardo Torres, Telecom Paris, France
Contacts