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Found 7 papers, 2 papers with code
Multi-scale temporal-frequency attention for music source separation
In recent years, deep neural networks (DNNs) based approaches have achieved the start-of-the-art performance for music source separation (MSS).
A two-step backward compatible fullband speech enhancement system
Speech enhancement methods based on deep learning have surpassed traditional methods.
A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement
In this work, a new causal U-net based multiple-in-multiple-out structure is proposed for real-time multi-channel speech enhancement.
TeCANet: Temporal-Contextual Attention Network for Environment-Aware Speech Dereverberation
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments.
Multi-channel Multi-frame ADL-MVDR for Target Speech Separation
Many purely neural network based speech separation approaches have been proposed to improve objective assessment scores, but they often introduce nonlinear distortions that are harmful to modern automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ADL-MVDR: All deep learning MVDR beamformer for target speech separation
Speech separation algorithms are often used to separate the target speech from other interfering sources.
Audio-visual Multi-channel Recognition of Overlapped Speech
Automatic speech recognition (ASR) of overlapped speech remains a highly challenging task to date.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
