2 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
DeePAQ: A Perceptual Audio Quality Metric Based On Foundational Models and Weakly Supervised Learning
Guanxin Jiang, Andreas Brendel, Pablo M. Delgado +1
This paper presents the Deep learning-based Perceptual Audio Quality metric (DeePAQ) for evaluating general audio quality. Our approach leverages metric learning together with the…
RF-GML: Reference-Free Generative Machine Listener
Arijit Biswas, Guanxin Jiang
This paper introduces a novel reference-free (RF) audio quality metric called the RF-Generative Machine Listener (RF-GML), designed to evaluate coded mono, stereo, and binaural aud…
Generative Machine Listener
Guanxin Jiang, Lars Villemoes, Arijit Biswas
We show how a neural network can be trained on individual intrusive listening test scores to predict a distribution of scores for each pair of reference and coded input stereo or b…
Stereo InSE-NET: Stereo Audio Quality Predictor Transfer Learned from Mono InSE-NET
Arijit Biswas, Guanxin Jiang
Automatic coded audio quality predictors are typically designed for evaluating single channels without considering any spatial aspects. With InSE-NET [1], we demonstrated mimicking…
InSE-NET: A Perceptually Coded Audio Quality Model based on CNN
Guanxin Jiang, Arijit Biswas, Christian Bergler +1
Automatic coded audio quality assessment is an important task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen codecs, bitrates, conte…