activity
20242026
most citedURGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement

30 citations · 42 across the 11 of their papers we have counts for

collaborators

11 papers

eess.AS2026

EffVOC: Low-Delay Efficient Speech Waveform Reconstruction from Spectral Representations Without Phase

Renzheng Shi, Simon Welker, Timo Gerkmann +1

The Griffin-Lim algorithm has been a seminal contribution for phase reconstruction from amplitude spectrograms, however, requiring (infinitely) high algorithmic delay. Its low-dela…

eess.AS2026

Knowledge Distillation for Efficient Acoustic Echo Control

Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt

In recent years, many efforts have been made to supersede classical acoustic echo control (AEC) algorithms with more powerful machine-learned approaches. While surpassing the perfo…

eess.AS2026

Noise-Robust AV-ASR Using Visual Features Both in the Whisper Encoder and Decoder

Zhengyang Li, Thomas Graave, Björn Möller +3

In audiovisual automatic speech recognition (AV-ASR) systems, information fusion of visual features in a pre-trained ASR has been proven as a promising method to improve noise robu…

cs.CL2026

Engineering of Hallucination in Generative AI: It's not a Bug, it's a Feature

Tim Fingscheidt, Patrick Blumenberg, Björn Möller

Generative artificial intelligence (AI) is conquering our lives at lightning speed. Large language models such as ChatGPT answer our questions or write texts for us, large computer…

cs.CV2025

OpenViGA: Video Generation for Automotive Driving Scenes by Streamlining and Fine-Tuning Open Source Models with Public Data

Björn Möller, Zhengyang Li, Malte Stelzer +4

Recent successful video generation systems that predict and create realistic automotive driving scenes from short video inputs assign tokenization, future state prediction (world m…

cs.LG2025

Improving Block-Wise LLM Quantization by 4-bit Block-Wise Optimal Float (BOF4): Analysis and Variations

Patrick Blumenberg, Thomas Graave, Tim Fingscheidt

Large language models (LLMs) demand extensive memory capacity during both fine-tuning and inference. To enable memory-efficient fine-tuning, existing methods apply block-wise quant…