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Found 9 papers, 2 papers with code
Frequency bin-wise single channel speech presence probability estimation using multiple DNNs
In addition, the noisy speech and the a posteriori probability SPP representation are used to train our model.
A Two-Stage Deep Representation Learning-Based Speech Enhancement Method Using Variational Autoencoder and Adversarial Training
To obtain a higher quality enhanced speech, we propose a two-stage DRL-based SE method through adversarial training.
Privacy-Preserving Distributed Expectation Maximization for Gaussian Mixture Model using Subspace Perturbation
Privacy has become a major concern in machine learning.
A deep representation learning speech enhancement method using $β$-VAE
In previous work, we proposed a variational autoencoder-based (VAE) Bayesian permutation training speech enhancement (SE) method (PVAE) which indicated that the SE performance of the traditional deep neural network-based (DNN) method could be improved by deep representation learning (DRL).
A Bayesian Permutation training deep representation learning method for speech enhancement with variational autoencoder
This means that the proposed method can apply the VAE to model both speech and noise signals, which is totally different from the previous VAE-based SE works.
Speech Decomposition Based on a Hybrid Speech Model and Optimal Segmentation
The segmentation relies on parameter estimates of a hybrid speech model and the maximum a posteriori (MAP) and log-likelihood criteria as rules for model selection among the possible segment lengths, for voiced and unvoiced speech, respectively.
Space Alternating Variational Estimation Based Sparse Bayesian Learning for Complex-value Sparse Signal Recovery Using Adaptive Laplace Priors
In experiments, the proposed algorithm is studied for complex Gaussian random dictionaries and different types of complex signals.
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
We therefore propose to insert noise in the non-convergent subspace through the dual variable such that the private data are protected, and the accuracy of the desired solution is completely unaffected.
A Variational EM Method for Pole-Zero Modeling of Speech with Mixed Block Sparse and Gaussian Excitation
The modeling of speech can be used for speech synthesis and speech recognition.
