28 citations · 92 across the 14 of their papers we have counts for
18 papers
Perceptual Quality Loss or Loss of Perceptual Quality?
Danilo de Oliveira, Tal Peer, Maurício do V. M. da Costa +1
Contemporary deep speech enhancement (SE) models are often trained with specific auxiliary terms in the loss function as a way to improve their performance in terms of perceptual m…
Livin' on a Prior: Likelihood Score Approximation for Inverse Problems
Rostislav Makarov, Tal Peer, Danilo de Oliveira +1
Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrained generative prior with a…
Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
Jakob Kienegger, Tal Peer, Sina Khanagha +1
Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization. Modern beamformers…
Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
Danilo de Oliveira, Tal Peer, Timo Gerkmann
Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is…
Real-Time Streamable Generative Speech Restoration with Flow Matching
Simon Welker, Bunlong Lay, Maris Hillemann +2
Diffusion-based generative models have greatly impacted the speech processing field in recent years, exhibiting high speech naturalness and spawning a new research direction. Their…
Are These Even Words? Quantifying the Gibberishness of Generative Speech Models
Danilo de Oliveira, Tal Peer, Jonas Rochdi +1
Significant research efforts are currently being dedicated to non-intrusive quality and intelligibility assessment, especially given how it enables curation of large scale datasets…
Real-Time Streaming Mel Vocoding with Generative Flow Matching
Simon Welker, Tal Peer, Timo Gerkmann
The task of Mel vocoding, i.e., the inversion of a Mel magnitude spectrogram to an audio waveform, is still a key component in many text-to-speech (TTS) systems today. Based on gen…
LipDiffuser: Lip-to-Speech Generation with Conditional Diffusion Models
Julius Richter, Danilo de Oliveira, Tal Peer +1
We present LipDiffuser, a conditional diffusion model for lip-to-speech generation synthesizing natural and intelligible speech directly from silent video recordings. Our approach…
Live Iterative Ptychography with projection-based algorithms
Simon Welker, Tal Peer, Henry N. Chapman +1
In this work, we demonstrate that the ptychographic phase problem can be solved in a live fashion during scanning, while data is still being collected. We propose a generally appli…
A Flexible Online Framework for Projection-Based STFT Phase Retrieval
Tal Peer, Simon Welker, Johannes Kolhoff +1
Several recent contributions in the field of iterative STFT phase retrieval have demonstrated that the performance of the classical Griffin-Lim method can be considerably improved…
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings
Danilo de Oliveira, Julius Richter, Jean-Marie Lemercier +2
Since its inception, the field of deep speech enhancement has been dominated by predictive (discriminative) approaches, such as spectral mapping or masking. Recently, however, nove…
Speech Signal Improvement Using Causal Generative Diffusion Models
Julius Richter, Simon Welker, Jean-Marie Lemercier +3
In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions. The method is based on a generative diffusion model w…
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…