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Found 6 papers, 1 papers with code
Encoding Speaker-Specific Latent Speech Feature for Speech Synthesis
To overcome previous limitations, we propose effective methods for feature learning and representing target speakers' speech characteristics by discretizing the features and conditioning them to a speech synthesis model.
VITS2: Improving Quality and Efficiency of Single-Stage Text-to-Speech with Adversarial Learning and Architecture Design
Single-stage text-to-speech models have been actively studied recently, and their results have outperformed two-stage pipeline systems.
SplitAMC: Split Learning for Robust Automatic Modulation Classification
Automatic modulation classification (AMC) is a technology that identifies a modulation scheme without prior signal information and plays a vital role in various applications, including cognitive radio and link adaptation.
New Versions of Gradient Temporal Difference Learning
Sutton, Szepesv\'{a}ri and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training.
Improving Accuracy of Binary Neural Networks using Unbalanced Activation Distribution
We also show that adjusting the threshold values of binary activation functions results in the unbalanced distribution of the binary activation, which increases the accuracy of BNN models.
Compensated Integrated Gradients to Reliably Interpret EEG Classification
Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction.
