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Found 5 papers, 1 papers with code
Integrating Uncertainty into Neural Network-based Speech Enhancement
In this paper, we study the benefits of modeling uncertainty in clean speech estimation.
Partially Adaptive Multichannel Joint Reduction of Ego-noise and Environmental Noise
In this paper, we propose a multichannel partially adaptive scheme to jointly model ego-noise and environmental noise utilizing the VAE-NMF framework, where we take advantage of spatially and spectrally structured characteristics of ego-noise by pre-training the ego-noise model, while retaining the ability to adapt to unknown environmental noise.
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models
Single-channel deep speech enhancement approaches often estimate a single multiplicative mask to extract clean speech without a measure of its accuracy.
Integrating Statistical Uncertainty into Neural Network-Based Speech Enhancement
Speech enhancement in the time-frequency domain is often performed by estimating a multiplicative mask to extract clean speech.
Variational Autoencoder for Speech Enhancement with a Noise-Aware Encoder
Recently, a generative variational autoencoder (VAE) has been proposed for speech enhancement to model speech statistics.
