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Found 6 papers, 2 papers with code
Interpretations of Domain Adaptations via Layer Variational Analysis
The method shows an increase in efficiency and accuracy for domain adaptation.
On the robustness of non-intrusive speech quality model by adversarial examples
It has been shown recently that deep learning based models are effective on speech quality prediction and could outperform traditional metrics in various perspectives.
Boosting Self-Supervised Embeddings for Speech Enhancement
We further study the relationship between the noise robustness of SSL representation via clean-noisy distance (CN distance) and the layer importance for SE.
Ranked #7 on
Speech Enhancement
on VoiceBank + DEMAND
Toward Real-World Voice Disorder Classification
Conclusion: By deploying factorized convolutional neural networks and domain adversarial training, domain-invariant features can be derived for voice disorder classification with limited resources.
Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport
This paper presents a novel discriminator-constrained optimal transport network (DOTN) that performs unsupervised domain adaptation for speech enhancement (SE), which is an essential regression task in speech processing.
SRECG: ECG Signal Super-resolution Framework for Portable/Wearable Devices in Cardiac Arrhythmias Classification
In our experiments, we downsampled the ECG signals from the CPSC2018 dataset and evaluated their HMC accuracies with and without the SRECG.
