Author

R. Olsson

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Preprint Sep 2026

Unsupervised Speech Enhancement via Drifting

This paper addresses unsupervised speech enhancement in the unpaired setting using drifting methods, where training relies on separate collections of degraded and clean audio without corresponding pairs. While recent drifting approaches enable unpaired training, they do so at a heavy cost: because the objective optimiz...

Diego Caviedes-Nozal, Liang Xu, R. Olsson et al. · 0 citations

#machine learning Preprint Sep 2026

Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement

Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontroller NPUs can accelerate neural inference under these constraints, but only through a restricted set of operators in static, integer-quantized graphs. Recent speech-enhancem...

Clément Laroche, R. Olsson · 0 citations

#artificial intelligence Preprint Sep 2026

DriftSE: Speech Enhancement with Generative Drifting

This work introduces dual-latent drifting, performing parallel drifting in both semantic and acoustic latents to simultaneously preserve phonetic intelligibility and acoustic fidelity and demonstrates that DriftSE enables fully unpaired training by aligning latent distributions rather than exact point-wise targets.

Liang Xu, Diego Caviedes-Nozal, W. Kleijn et al. · 1 citation