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Found 13 papers, 6 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.
Wind Noise Reduction with a Diffusion-based Stochastic Regeneration Model
We show that our stochastic regeneration model outperforms other neural-network-based wind noise reduction methods as well as purely predictive and generative models, on a dataset using simulated and real-recorded wind noise.
Diffusion Posterior Sampling for Informed Single-Channel Dereverberation
We present in this paper an informed single-channel dereverberation method based on conditional generation with diffusion models.
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.
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.
Extending DNN-based Multiplicative Masking to Deep Subband Filtering for Improved Dereverberation
We demonstrate that the resulting deep subband filtering scheme outperforms multiplicative masking for dereverberation, while leaving the denoising performance virtually the same.
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
Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments
In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman filtering variant of the weighted prediction error (WPE) method is proposed.
Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices
This work focuses on online dereverberation for hearing devices using the weighted prediction error (WPE) algorithm.
A neural network-supported two-stage algorithm for lightweight dereverberation on hearing devices
By deriving new metrics analyzing the dereverberation performance in various time ranges, we confirm that directly optimizing for a criterion at the output of the multi-channel linear filtering stage results in a more efficient dereverberation as compared to placing the criterion at the output of the DNN to optimize the PSD estimation.
