This special session focuses on various extensions of the NMF models designed to solve audio-specific problems, including monaural and multi-channel audio source separation, voice conversion, audio event detection, music transcription and speech analysis.
Hirokazu Kameoka, Communication Science Laboratories NTT, Japan
Alexey Ozerov, Technicolor Research and Innovation, France
Cédric Févotte, Institut de Recherche en Informatique de Toulouse (IRIT), France
Paris Smaragdis, University of Illinois at Urbana-Champaign, USA
This special session will bring together papers on advanced machine learning approaches to various imaging problems, with a particular focus on inverse problems aka image reconstruction problems. The approaches presented in the session will exploit sophisticated signal models and image reconstruction models based on low-rank, sparsity, tensor and manifold structures, graphical, and convolutional models, etc., together with modern large-scale optimization techniques for learning and incorporating the models in imaging applications. Diverse imaging modalities and systems will be covered, with a focus on computational approaches.
The session would bring together leading researchers and field experts in computational imaging, biomedical imaging, machine learning, image processing, compressed sensing, and optimization, and facilitate substantive and cross-disciplinary discussions on cutting-edge data-driven imaging and image reconstruction approaches.
Main Focus Topics of the Session:
- Computational imaging, Inverse problems, Image processing, Image reconstruction, Magnetic resonance imaging, Computed tomography, Dynamic imaging, Optical imaging, Computational photography, Compressed sensing
- Sparse signal models, Dictionary Learning, Low-rank models, Tensor models, Manifold models, Convolutional models, Deep learning, Other machine learning techniques
- Optimization methods, and Performance guarantees for data-driven techniques
Saiprasad Ravishankar, University of Michigan
Brendt Wohlberg, Los Alamos National Lab
Stamatios Lefkimmiatis, Skoltech
Jong Chul Ye, KAIST, South Korea
Scope: Deep learning, multi-task learning, ensemble learning, speech enhancement, speech separation, dereverberation, denoising, single channel, multiple channels, feature design, neural network architectures, loss functions, joint enhancement and recognition networks, beamforming networks, model generalization, post-processing, integration of deep learning and other approaches, customized applications
Hakan Erdogan, Microsoft
Jun Du, University of Science and Technology of China, China