5 papers
Unsupervised Speech Enhancement via Drifting
Diego Caviedes-Nozal, Liang Xu, Rasmus Kongsgaard Olsson +1
This paper addresses unsupervised speech enhancement in the unpaired setting using drifting methods, where training relies on separate collections of degraded and clean audio witho…
Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement
Clément Laroche, Rasmus Kongsgaard Olsson
Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontroller NPUs can accelerate neural i…
DriftSE: Speech Enhancement with Generative Drifting
Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn +2
We propose DriftSE, a novel one-step generative framework for speech enhancement formulated as a latent distribution equilibrium problem. During training, the drifting field aligns…
Speech Enhancement Based on Drifting Models
Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn +2
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…
Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering
Ernst Seidel, Rasmus Kongsgaard Olsson, Karim Haddad +3
Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (…