28 citations · 92 across the 14 of their papers we have counts for
4 papers · 1 filter
Perceptual Quality Loss or Loss of Perceptual Quality?
Danilo de Oliveira, Tal Peer, Maurício do V. M. da Costa +1
Contemporary deep speech enhancement (SE) models are often trained with specific auxiliary terms in the loss function as a way to improve their performance in terms of perceptual m…
Livin' on a Prior: Likelihood Score Approximation for Inverse Problems
Rostislav Makarov, Tal Peer, Danilo de Oliveira +1
Generative models have found great success as data-driven methods of solving inverse problems. Two popular approaches work either by combining a pretrained generative prior with a…
Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics
Jakob Kienegger, Tal Peer, Sina Khanagha +1
Linear spatial filters (beamformers) enable robust, generalizable and interpretable speech enhancement with performance guarantees under ideal parameterization. Modern beamformers…
Too Good to Be True: A Study on Modern Automatic Speech Recognition for the Evaluation of Speech Enhancement
Danilo de Oliveira, Tal Peer, Timo Gerkmann
Speech enhancement (SE) systems are typically evaluated using a variety of instrumental metrics. The use of automatic speech recognition (ASR) systems to evaluate SE performance is…