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Noise-robust Speech Separation with Fast Generative Correction
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Speech separation, the task of isolating multiple speech sources from a mixed audio signal, remains challenging in noisy environments. In this paper, we propose a generative correction method to enhance the output of a discriminative separator. By leveraging a generative corrector based on a diffusion model, we refine the separation process for single-channel mixture speech by removing noises and perceptually unnatural distortions. Furthermore, we optimize the generative model using a predictive loss to streamline the diffusion model's reverse process into a single step and rectify any associated errors by the reverse process. Our method achieves state-of-the-art performance on the in-domain Libri2Mix noisy dataset, and out-of-domain WSJ with a variety of noises, improving SI-SNR by 22-35% relative to SepFormer, demonstrating robustness and strong generalization capabilities.
Forward citations
Cited by 2 Pith papers
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Source Separation by Flow Matching
FLOSS applies flow matching on the subspace orthogonal to the mixture average to generate source-separated signals that exactly sum to the observed mixture.
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SoloSpeech: Enhancing Intelligibility and Quality in Target Speech Extraction through a Cascaded Generative Pipeline
A cascaded pipeline of audio compression, latent diffusion extraction, and generative correction achieves state-of-the-art target speech extraction quality and intelligibility on Libri2Mix and out-of-domain data.
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