![]() The first stage (AEC) yields an echo estimate, which -Īs a novelty for a DNN AEC model - is further used by the second stage to Two-stage model (Y$^2$-Net) which consists of two FCRNs, each with two inputsĪnd one output (Y-Net). The lack of a separate loss for echo estimation. To a noticeable decline in the quality of the near-end speech component due to However, the combination ofĪEC, postfiltering, and noise suppression to a single network typically leads Recurrent network (FCRN) structure, which has already proven its performance onīoth noise suppression and AEC tasks, individually. A promising network topology is a fully convolutional The proposed modelsĪchieved remarkable performance for the separate tasks of AEC and residual echo To traditional acoustic echo cancellation (AEC) algorithms. Authors: Ernst Seidel, Jan Franzen, Maximilian Strake, Tim Fingscheidt Download PDF Abstract: In recent years, deep neural networks (DNNs) were studied as an alternative
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