28 citations · 92 across the 14 of their papers we have counts for
6 papers · 1 filter
DiffPhase: Generative Diffusion-based STFT Phase Retrieval
Tal Peer, Simon Welker, Timo Gerkmann
Diffusion probabilistic models have been recently used in a variety of tasks, including speech enhancement and synthesis. As a generative approach, diffusion models have been shown…
Efficient Transformer-based Speech Enhancement Using Long Frames and STFT Magnitudes
Danilo de Oliveira, Tal Peer, Timo Gerkmann
The SepFormer architecture shows very good results in speech separation. Like other learned-encoder models, it uses short frames, as they have been shown to obtain better performan…
Beyond Griffin-Lim: Improved Iterative Phase Retrieval for Speech
Tal Peer, Simon Welker, Timo Gerkmann
Phase retrieval is a problem encountered not only in speech and audio processing, but in many other fields such as optics. Iterative algorithms based on non-convex set projections…
Integrating Statistical Uncertainty into Neural Network-Based Speech Enhancement
Huajian Fang, Tal Peer, Stefan Wermter +1
Speech enhancement in the time-frequency domain is often performed by estimating a multiplicative mask to extract clean speech. However, most neural network-based methods perform p…
Phase-Aware Deep Speech Enhancement: It's All About The Frame Length
Tal Peer, Timo Gerkmann
Algorithmic latency in speech processing is dominated by the frame length used for Fourier analysis, which in turn limits the achievable performance of magnitude-centric approaches…
Deep Iterative Phase Retrieval for Ptychography
Simon Welker, Tal Peer, Henry N. Chapman +1
One of the most prominent challenges in the field of diffractive imaging is the phase retrieval (PR) problem: In order to reconstruct an object from its diffraction pattern, the in…