Video Multimethod Assessment Fusion (VMAF) [1], [2], [3] is a popular tool in the industry for measuring coded video quality. In this study, we propose an auditory-inspired frontend in existing VMAF for creating videos of reference and coded spectrograms, and extended VMAF for measuring coded audio quality. We name our system AudioVMAF. We demonstrate that image replication is capable of further enhancing prediction accuracy, especially when band-limited anchors are present. The proposed method significantly outperforms all existing visual quality features repurposed for audio, and even demonstrates a significant overall improvement of 7.8% and 2.0% of Pearson and Spearman rank correlation coefficient, respectively, over a dedicated audio quality metric (ViSQOL-v3 [4]) also inspired from the image domain.
@article{arxiv.2308.03437,
title = {AudioVMAF: Audio Quality Prediction with VMAF},
author = {Arijit Biswas and Harald Mundt},
journal= {arXiv preprint arXiv:2308.03437},
year = {2023}
}
Comments
Accepted to 155th Audio Engineering Society (AES), New York, NY, USA, October 2023