Speech Enhancement Based on Drifting Models

• URL: http://arxiv.org/abs/2604.24199v1
• Date: Mon, 27 Apr 2026 09:00:51 GMT
• Title: Speech Enhancement Based on Drifting Models
• Abstract summary: We propose Speech Enhancement based on Drifting Models (DriftSE)<n>DriftSE is a novel generative framework that formulates denoising as an equilibrium problem.<n> Experiments on the VoiceBank-DEMAND demonstrate that DriftSE achieves high-fidelity enhancement in a single step.
• Score: 2.6958419576949146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative sampling, DriftSE natively achieves one-step inference by evolving the pushforward distribution of a mapping function to directly match the clean speech distribution. This evolution is driven by a Drifting Field, a learned correction vector that guides samples toward the high-density regions of the clean distribution, which naturally facilitates training on unpaired data by matching distributions rather than paired samples. We investigate the framework under two formulations: a direct mapping from the noisy observation, and a stochastic conditional generative model from a Gaussian prior. Experiments on the VoiceBank-DEMAND benchmark demonstrate that DriftSE achieves high-fidelity enhancement in a single step, outperforming multi-step diffusion baselines and establishing a new paradigm for speech enhancement.

Related papers

• Enhanced Low-Density Region Exploration in Classifier-Guided Diffusion Models Through Modified Reverse Diffusion Sampling [5.191572579366909]
  We propose a sampling-time, density-aware extension of conditional diffusion model.<n>We modify the guided reverse dynamics by steering trajectories toward low-confidence regions.<n>We show that the proposed sampler consistently improves ADM model recall at 64x64 resolution while maintaining a comparable FID.
  arXiv  Detail & Related papers  (2026-06-11T13:35:53Z)
• TAG:Tangential Amplifying Guidance for Hallucination-Resistant Diffusion Sampling [53.61290359948953]
  Tangential Amplifying Guidance (TAG) operates solely on trajectory signals without modifying the underlying diffusion model.<n>We formalize this guidance process by leveraging a first-order Taylor expansion.<n> TAG is a plug-and-play, architecture-agnostic module that improves diffusion sampling fidelity with minimal computational addition.
  arXiv  Detail & Related papers  (2025-10-06T06:53:29Z)
• Test-Time Anchoring for Discrete Diffusion Posterior Sampling [38.507644561076894]
  Posterior sampling is a challenging problem for pretrained discrete diffusion foundation models.<n>We introduce Anchored Posterior Sampling (APS) for masked diffusion foundation models.<n>Our approach achieves state-of-the-art performance among discrete diffusion samplers across linear and nonlinear inverse problems.
  arXiv  Detail & Related papers  (2025-10-02T17:58:37Z)
• G$^2$RPO: Granular GRPO for Precise Reward in Flow Models [74.21206048155669]
  We propose a novel Granular-GRPO (G$2$RPO) framework that achieves precise and comprehensive reward assessments of sampling directions.<n>We introduce a Multi-Granularity Advantage Integration module that aggregates advantages computed at multiple diffusion scales.<n>Our G$2$RPO significantly outperforms existing flow-based GRPO baselines.
  arXiv  Detail & Related papers  (2025-10-02T12:57:12Z)
• Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling [70.8832906871441]
  We study how to steer generation toward desired rewards without retraining the models.<n>Prior methods typically resample or filter within a single denoising trajectory, optimizing rewards step-by-step without trajectory-level refinement.<n>We introduce particle Gibbs sampling for diffusion language models (PG-DLM), a novel inference-time algorithm enabling trajectory-level refinement while preserving generation perplexity.
  arXiv  Detail & Related papers  (2025-07-11T08:00:47Z)
• Acoustic Waveform Inversion with Image-to-Image Schrödinger Bridges [0.0]
  We introduce a conditional Image-to-Image Schr"odinger Bridge (c$textI2textSB$) framework to generate high-resolution samples.<n>Our experiments demonstrate that the proposed solution outperforms our reimplementation of conditional diffusion model.
  arXiv  Detail & Related papers  (2025-06-18T10:55:26Z)
• Consistent World Models via Foresight Diffusion [56.45012929930605]
  We argue that a key bottleneck in learning consistent diffusion-based world models lies in the suboptimal predictive ability.<n>We propose Foresight Diffusion (ForeDiff), a diffusion-based world modeling framework that enhances consistency by decoupling condition understanding from target denoising.
  arXiv  Detail & Related papers  (2025-05-22T10:01:59Z)
• Your Diffusion Model is Secretly a Noise Classifier and Benefits from Contrastive Training [20.492630610281658]
  Diffusion models learn to denoise data and the trained denoiser is then used to generate new samples from the data distribution. We introduce a new self-supervised training objective that differentiates the levels of noise added to a sample. We show by diverse experiments that the proposed contrastive diffusion training is effective for both sequential and parallel settings.
  arXiv  Detail & Related papers  (2024-07-12T03:03:50Z)
• Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion [61.03681839276652]
  Diffusion Forcing is a new training paradigm where a diffusion model is trained to denoise a set of tokens with independent per-token noise levels.<n>We apply Diffusion Forcing to sequence generative modeling by training a causal next-token prediction model to generate one or several future tokens.
  arXiv  Detail & Related papers  (2024-07-01T15:43:25Z)
• Towards Controllable Diffusion Models via Reward-Guided Exploration [15.857464051475294]
  We propose a novel framework that guides the training-phase of diffusion models via reinforcement learning (RL) RL enables calculating policy gradients via samples from a pay-off distribution proportional to exponential scaled rewards, rather than from policies themselves. Experiments on 3D shape and molecule generation tasks show significant improvements over existing conditional diffusion models.
  arXiv  Detail & Related papers  (2023-04-14T13:51:26Z)
• A Study on Speech Enhancement Based on Diffusion Probabilistic Model [63.38586161802788]
  We propose a diffusion probabilistic model-based speech enhancement model (DiffuSE) model that aims to recover clean speech signals from noisy signals. The experimental results show that DiffuSE yields performance that is comparable to related audio generative models on the standardized Voice Bank corpus task.
  arXiv  Detail & Related papers  (2021-07-25T19:23:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.