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Found 13 papers, 8 papers with code
Diffusion Models for Audio Restoration
Here, we aim to show that diffusion models can combine the best of both worlds and offer the opportunity to design audio restoration algorithms with a good degree of interpretability and a remarkable performance in terms of sound quality.
Single and Few-step Diffusion for Generative Speech Enhancement
While the performance of usual generative diffusion algorithms drops dramatically when lowering the number of function evaluations (NFEs) to obtain single-step diffusion, we show that our proposed method keeps a steady performance and therefore largely outperforms the diffusion baseline in this setting and also generalizes better than its predictive counterpart.
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings
Since its inception, the field of deep speech enhancement has been dominated by predictive (discriminative) approaches, such as spectral mapping or masking.
Audio-Visual Speech Enhancement with Score-Based Generative Models
This paper introduces an audio-visual speech enhancement system that leverages score-based generative models, also known as diffusion models, conditioned on visual information.
Audio-Visual Speech Separation in Noisy Environments with a Lightweight Iterative Model
We propose Audio-Visual Lightweight ITerative model (AVLIT), an effective and lightweight neural network that uses Progressive Learning (PL) to perform audio-visual speech separation in noisy environments.
Speech Signal Improvement Using Causal Generative Diffusion Models
In this paper, we present a causal speech signal improvement system that is designed to handle different types of distortions.
Reducing the Prior Mismatch of Stochastic Differential Equations for Diffusion-based Speech Enhancement
Recently, score-based generative models have been successfully employed for the task of speech enhancement.
StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation
As diffusion models are generative approaches they may also produce vocalizing and breathing artifacts in adverse conditions.
Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration
In this paper, we systematically compare the performance of generative diffusion models and discriminative approaches on different speech restoration tasks.
Speech Enhancement and Dereverberation with Diffusion-based Generative Models
This matches our forward process which moves from clean speech to noisy speech by including a drift term.
Ranked #19 on
Speech Enhancement
on VoiceBank + DEMAND
Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain
Score-based generative models (SGMs) have recently shown impressive results for difficult generative tasks such as the unconditional and conditional generation of natural images and audio signals.
Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement
In this work, we propose to use an adversarial training scheme for variational autoencoders to disentangle the label from the other latent variables.
Guided Variational Autoencoder for Speech Enhancement With a Supervised Classifier
In this paper, we propose to guide the variational autoencoder with a supervised classifier separately trained on noisy speech.
