1 citations · 1 across the 5 of their papers we have counts for
6 papers
Shortcut Flow Matching for Speech Enhancement: Step-Invariant flows via single stage training
Naisong Zhou, Saisamarth Rajesh Phaye, Milos Cernak +4
Diffusion-based generative models have achieved state-of-the-art performance for perceptual quality in speech enhancement (SE). However, their iterative nature requires numerous Ne…
DeepFilterGAN: A Full-band Real-time Speech Enhancement System with GAN-based Stochastic Regeneration
Sanberk Serbest, Tijana Stojkovic, Milos Cernak +1
In this work, we propose a full-band real-time speech enhancement system with GAN-based stochastic regeneration. Predictive models focus on estimating the mean of the target distri…
Model as Loss: A Self-Consistent Training Paradigm
Saisamarth Rajesh Phaye, Milos Cernak, Andrew Harper
Conventional methods for speech enhancement rely on handcrafted loss functions (e.g., time or frequency domain losses) or deep feature losses (e.g., using WavLM or wav2vec), which…
Diffusion-based Speech Enhancement with Schrödinger Bridge and Symmetric Noise Schedule
Siyi Wang, Siyi Liu, Andrew Harper +3
Recently, diffusion-based generative models have demonstrated remarkable performance in speech enhancement tasks. However, these methods still encounter challenges, including the l…
On real-time multi-stage speech enhancement systems
Lingjun Meng, Jozef Coldenhoff, Paul Kendrick +4
Recently, multi-stage systems have stood out among deep learning-based speech enhancement methods. However, these systems are always high in complexity, requiring millions of param…
Multi-Channel MOSRA: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and a Teacher Model
Jozef Coldenhoff, Andrew Harper, Paul Kendrick +2
Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are p…