Predicting Audio Quality for different assessor types using machine learning

Volk CP, Nordby J, Stegenborg-Andersen T, Zacharov N. Predicting Audio Quality for different assessor types using machine learning. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 10494. Available from: https://aes.org/publications/elibrary-page/?id=21171

@inproceedings{Volk2020_21171,
  author = {Volk, Christer P. and Nordby, Jon and Stegenborg-Andersen, Tore and Zacharov, Nick},
  title = {{Predicting Audio Quality for different assessor types using machine learning}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 10494},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21171}
}

TY  - CPAPER
TI  - Predicting Audio Quality for different assessor types using machine learning
AU  - Volk, Christer P.
AU  - Nordby, Jon
AU  - Stegenborg-Andersen, Tore
AU  - Zacharov, Nick
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 10494
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21171
PB  - Audio Engineering Society
LA  - en
AB  - In this paper we study how sound quality is evaluated by different groups of assessors, with different levels of hearing loss. Formal listening tests using the Basic Audio Quality scale were designed using 22 headphones spanning a wide range of qualities and sound quality characteristics. The tests were performed with two formally selected listening panels with normal hearing (NH), and mild (N2) or moderate (N3) hearing loss characteristics. It is shown that not only do the two panels evaluate the sound quality consistently within each panel, but also that there are systematic changes in the manner in which hearing loss impacts the evaluation and ranking of the devices under study. Using this data we successfully train machine learning algorithms to predict the sound quality for the two assessor type panels. The prediction performance for each panel is NH: RMSE = 7.1 ± 3.0, PCC = 0.91 ± 0.13; HI: RMSE = 8.7 ± 2.4, PCC = 0.91 ± 0.12. Whilst it may not be practical to run listening tests with multiple panels of assessors, we demonstrate here that machine learning based models can be practically and cost effectively employed to predict the perception of multiple assessor groups rapidly and simultaneously.
ER  -