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Found 4 papers, 2 papers with code
NeuroHeed+: Improving Neuro-steered Speaker Extraction with Joint Auditory Attention Detection
Neuro-steered speaker extraction aims to extract the listener's brain-attended speech signal from a multi-talker speech signal, in which the attention is derived from the cortical activity.
Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction
Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers.
Speech Denoising with Deep Feature Losses
We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly.
Speech Denoising Convolutional Neural Network trained with Deep Feature Losses.
We present an end-to-end deep learning approach to denoising speech signals by processing the raw waveform directly.
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