Name
Abstract | ||
|---|---|---|
We propose a method for nonlinear residual echo suppression that consists of extracting spectral features from the far-end signal, and using an artificial neural network to model the residual echo magnitude spectrum from these features. We compare the modeling accuracy achieved by realizations with different features and network topologies, evaluating the mean squared error of the estimated residual echo magnitude spectrum. We also present a low complexity real-time implementation combining an offline-trained network with online adaptation, and investigate its performance in terms of echo suppression and speech distortion for real mobile phone recordings. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/WASPAA.2013.6701825 | WASPAA |
Keywords | Field | DocType |
echo suppression,feature extraction,mean square error methods,mobile radio,neural nets,real-time systems,artificial neural network,far-end signal,feature extraction,mean squared error,mobile phone,network topology,nonlinear residual echo suppression,real-time implementation,residual echo magnitude spectrum,spectral feature,speech distortion,AES,Nonlinear acoustic echo suppression,RES,residual echo suppression | Mobile radio,Residual,Nonlinear system,Computer science,Mean squared error,Electronic engineering,Feature extraction,Network topology,Feature based,Acoustics,Artificial neural network | Conference |
ISSN | Citations | PageRank |
1931-1168 | 7 | 0.49 |
References | Authors | |
11 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Schwarz | 1 | 29 | 2.73 |
| Christian Hofmann | 2 | 32 | 5.83 |
| W. Kellermann | 3 | 686 | 71.03 |