Published in ICASSP 2023, 2023
Recommended citation: Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann (2023). "Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration." ICASSP 2023. https://arxiv.org/abs/2211.02397
Published in ICASSP 2023, 2023
Recommended citation: Tal Peer, Simon Welker, Timo Gerkmann (2023). "DiffPhase: Generative Diffusion-based STFT Phase Retrieval." ICASSP 2023. https://arxiv.org/abs/2211.04332
Published in arXiv preprint arXiv:2212.11851, 2022
Recommended citation: Jean-Marie Lemercier, Julius Richter, Simon Welker, Timo Gerkmann (2022). "StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation" arXiv preprint arXiv:2212.11851. https://arxiv.org/abs/2212.11851
Published in The Symbiosis of Deep Learning and Differential Equations II, 2022
Recommended citation: Simon Welker, Henry N. Chapman, Timo Gerkmann (2022). "Blind Drifting: Diffusion models with a linear SDE drift term for blind image restoration tasks." NeurIPS 2022 Workshop The Symbiosis of Deep Learning and Differential Equations II. https://openreview.net/forum?id=VCLnhfPVEB
Published in INTERSPEECH 2022, 2022
Recommended citation: Simon Welker, Julius Richter, Timo Gerkmann (2022). "Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain." INTERSPEECH 2022. https://arxiv.org/abs/2203.17004
Published in IWAENC 2022, 2022
Recommended citation: Tal Peer, Simon Welker, Timo Gerkmann (2022). "Beyond Griffin-Lim: Improved Iterative Phase Retrieval for Speech." IWAENC 2022. https://arxiv.org/abs/2205.05496
Published in arXiv preprint arXiv:2208.05830, 2022
Recommended citation: Julius Richter, Simon Welker, Jean-Marie Lemercier, Bunlong Lay, Timo Gerkmann (2022). "Speech Enhancement and Dereverberation with Diffusion-based Generative Models." arXiv preprint arXiv:2208.05830. https://arxiv.org/abs/2208.05830
Published in ICASSP 2022, 2022
Recommended citation: Simon Welker, Tal Peer, Henry N. Chapman, Timo Gerkmann (2022). "Deep Iterative Phase Retrieval for Ptychography." ICASSP 2022. https://arxiv.org/abs/2202.10573
Published in Computer Physics Communications, 2021
CMInject simulates nanoparticle injection experiments of particles with diameters in the micrometer to nanometer-regime, e.g., for single-particle-imaging experiments.
Recommended citation: Simon Welker, Muhamed Amin, Jochen Küpper (2021). "CMInject: Python framework for the numerical simulation of nanoparticle injection pipelines." Computer Physics Communications. https://www.sciencedirect.com/science/article/abs/pii/S0010465521002502