New One-Step Speech Enhancement Method Using Drifting Models Outperforms Multi-Step Diffusion Approaches
Researchers have developed DriftSE, a novel speech enhancement framework that achieves high-quality audio denoising in a single step rather than requiring multiple iterative steps. The method formulates denoising as an equilibrium problem using a learned Drifting Field to guide noisy speech toward clean speech distributions. This approach demonstrates superior performance on standard benchmarks and introduces a new paradigm for efficient speech enhancement.
DriftSE proposes a generative framework for speech enhancement that fundamentally differs from existing multi-step diffusion approaches by enabling one-step inference. Rather than iteratively sampling to denoise audio, the method evolves a mapping function's pushforward distribution to directly match the clean speech distribution, guided by a learned correction vector called the Drifting Field. The framework operates under two formulations: direct mapping from noisy observations and stochastic conditional generation from a Gaussian prior. Testing on the VoiceBank-DEMAND benchmark shows DriftSE achieves high-fidelity enhancement while outperforming multi-step diffusion baselines. A key advantage is the method's ability to train on unpaired data by matching distributions rather than requiring paired noisy-clean speech samples, which addresses a practical limitation in speech enhancement research.
What's missing
The paper does not discuss computational complexity or inference time comparisons with baseline methods, which would be relevant for practical deployment. Additionally, generalization performance on datasets outside VoiceBank-DEMAND and robustness to unseen noise types are not addressed.
What different sources said
- arXiv cs.AICenter
Speech Enhancement Based on Drifting Models
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