A curation pipeline combining diffusion-based synthetic mixtures with a discriminative classifier produces and releases FSD50K-Solo, a single-source subset of FSD50K that matches human expert labels on a test set.
Less is more: Data curation matters in scaling speech enhance- ment.arXiv preprint arXiv:2506.23859
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L3-SE reduces linguistic hallucination in LM-based speech enhancement by distilling noise-invariant acoustic-semantic representations from noisy inputs to condition an autoregressive decoder-only language model.
Replacing early-reflected speech with time-shifted anechoic clean speech as the training target, combined with a two-stage distortion-perception framework, yields state-of-the-art universal speech enhancement.
citing papers explorer
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FSD50K-Solo: Automated Curation of Single-Source Sound Events
A curation pipeline combining diffusion-based synthetic mixtures with a discriminative classifier produces and releases FSD50K-Solo, a single-source subset of FSD50K that matches human expert labels on a test set.
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Reducing Linguistic Hallucination in LM-Based Speech Enhancement via Noise-Invariant Acoustic-Semantic Distillation
L3-SE reduces linguistic hallucination in LM-based speech enhancement by distilling noise-invariant acoustic-semantic representations from noisy inputs to condition an autoregressive decoder-only language model.
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Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement
Replacing early-reflected speech with time-shifted anechoic clean speech as the training target, combined with a two-stage distortion-perception framework, yields state-of-the-art universal speech enhancement.