activity
20222026
most citedNeural Kalman Filters for Acoustic Echo Cancellation

12 citations · 12 across the 7 of their papers we have counts for

collaborators

8 papers

eess.AS2026

DiffVQE2: An Efficient Low-delay Diffusion Model for Acoustic Echo and Noise Control

Haljan Lugo, Ernst Seidel, Pejman Mowlaee +2

Hands-free communication devices and speakerphones are inherently affected by acoustic echo and background noise. To mitigate these impairments, end-to-end discriminatively trained…

cs.SD2026

Perceptually Better, Semantically Worse: Measuring Speech Enhancement Impact on LLM-Based Voice Systems

Randy Frans Fela, Pejman Mowlaee

Speech enhancement (SE) is commonly applied as a preprocessing step in spoken AI pipelines under the assumption that better audio quality improves downstream task performance. Whet…

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

DiffVQE: Hybrid Diffusion Voice Quality Enhancement Under Acoustic Echo and Noise

Haljan Lugo, Ernst Seidel, Pejman Mowlaee +2

Acoustic echo and background noise pose challenges on speech enhancement in hands-free systems and speakerphones. Discriminatively trained end-to-end methods represent a powerful s…

eess.AS2025★ 12 cited

Neural Kalman Filters for Acoustic Echo Cancellation

Ernst Seidel, Gerald Enzner, Pejman Mowlaee +1

Kalman filtering is a powerful approach to adaptive filtering for various problems in signal processing. The frequency-domain adaptive Kalman filter (FDKF), based on the concept of…

eess.AS2024

Efficient High-Performance Bark-Scale Neural Network for Residual Echo and Noise Suppression

Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt

In recent years, the introduction of neural networks (NNs) into the field of speech enhancement has brought significant improvements. However, many of the proposed methods are quit…

eess.AS2023

Efficient Acoustic Echo Suppression with Condition-Aware Training

Ernst Seidel, Pejman Mowlaee, Tim Fingscheidt

The topic of deep acoustic echo control (DAEC) has seen many approaches with various model topologies in recent years. Convolutional recurrent networks (CRNs), consisting of a conv…

eess.AS2022

Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering

Ernst Seidel, Rasmus Kongsgaard Olsson, Karim Haddad +3

Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (…