12 citations · 12 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…
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…