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Audio-visual VAE for Speech Enhancement
https://team.inria.fr/perception/research/av-vae-se/
We develop a conditional VAE (CVAE) where the audio speech generative process is conditioned on visual information of the lip region. At test time, the audio-visual speech generative model is combined with a noise model based on nonnegative matrix factorization, and speech enhancement relies on a Monte Carlo expectation-maximization algorithm.
Mixture of Inference Networks for VAE-based Audio-visual ...
https://team.inria.fr/robotlearn/mixture-of-inference-networks-for-vae-based-audio-visual-speech-enhancement/
In VAEs, the posterior of the latent variables is computationally intractable, and it is approximated by a so-called encoder network. Motivated by the fact that visual data, i.e. lip images of the speaker, provide helpful and complementary information about speech, some audio-visual architectures have been recently proposed.
Variational Autoencoder with CCA for Audio-Visual Cross ...
https://deepai.org/publication/variational-autoencoder-with-cca-for-audio-visual-cross-modal-retrieval
In this paper, we present a novel variational autoencoder (VAE) architecture for audio-visual cross-modal retrieval, by learning paired audio-visual correlation embedding and category correlation embedding as constraints to reinforce the mutuality of audio-visual information. On the one hand, audio encoder and visual encoder separately encode audio data …
Mixture of Inference Networks for VAE-based Audio-visual ...
https://deepai.org/publication/mixture-of-inference-networks-for-vae-based-audio-visual-speech-enhancement
In this paper, we are interested in unsupervised speech enhancement using latent variable generative models. We propose to learn a generative model for clean speech spectrogram based on a variational autoencoder (VAE) where a mixture of audio and visual networks is used to infer the posterior of the latent variables. This is motivated by the fact that …
Mixture of Inference Networks for VAE-based Audio …
https://arxiv.org/abs/1912.10647
Two encoder networks input, respectively, audio and visual data, and the posterior of the latent variables is modeled as a mixture of two Gaussian distributions output from each encoder network. The mixture variable is also latent, and therefore the inference of learning the optimal balance between the audio and visual inference networks is unsupervised as well. By …
Mixture of Inference Networks for VAE-Based Audio …
https://ieeexplore.ieee.org/document/9380713
Mixture of Inference Networks for VAE-Based Audio-Visual Speech Enhancement. Abstract:We address unsupervised audio-visual speech enhancement based on variational autoencoders (VAEs), where the prior distribution of clean speech spectrogram is simulated using an encoder-decoder architecture. At enhancement (test) time, the trained generative model …
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