1 citations · 3 across the 13 of their papers we have counts for
5 papers · 1 filter
Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric
Jyrki Alakuijala, Martin Bruse, Sami Boukortt +2
This paper introduces Zimtohrli, a novel, full-reference audio similarity metric designed for efficient and perceptually accurate quality assessment. In an era dominated by computa…
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…
Low-latency Assistive Audio Enhancement for Neurodivergent People
Alexander Popescu, Rosie Frost, Milos Cernak
Neurodivergent people frequently experience decreased sound tolerance, with estimates suggesting it affects 50-70% of this population. This heightened sensitivity can provoke react…
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…