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Found 4 papers, 0 papers with code
Voice Activity Detection for Transient Noisy Environment Based on Diffusion Nets
A deep neural network, which is trained to separate speech from non-speech frames, is obtained by concatenating the decoder to the encoder, resembling the known Diffusion nets architecture.
Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression
In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain.
Nonlinear Acoustic Echo Cancellation with Deep Learning
Second, the network is succeeded by a standard adaptive linear filter that constantly tracks the echo path between the loudspeaker output and the microphone.
Evaluation of Deep-Learning-Based Voice Activity Detectors and Room Impulse Response Models in Reverberant Environments
To mitigate this mismatch between training data and real data, we simulate an augmented training set that contains nearly five million utterances.
