AudioVMAF: Audio Quality Prediction with VMAF

Biswas A, Mundt H. AudioVMAF: Audio Quality Prediction with VMAF. In: AES Convention 155. Audio Engineering Society; 2023. Paper 180. Available from: https://aes.org/publications/elibrary-page/?id=22334

@inproceedings{Biswas2023_22334,
  author = {Biswas, Arijit and Mundt, Harald},
  title = {{AudioVMAF: Audio Quality Prediction with VMAF}},
  booktitle = {AES Convention 155},
  note = {Paper 180},
  year = {2023},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22334}
}

TY  - CPAPER
TI  - AudioVMAF: Audio Quality Prediction with VMAF
AU  - Biswas, Arijit
AU  - Mundt, Harald
T2  - AES Convention 155
M1  - Paper 180
PY  - 2023
DA  - 2023/10/06
UR  - https://aes.org/publications/elibrary-page/?id=22334
PB  - Audio Engineering Society
LA  - en
AB  - 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.
ER  -