A Computationally Efficient Model for Combined Assessment of Monaural and Binaural Audio Quality

Eurich B, Ewert SD, Dietz M, Biberger T. A Computationally Efficient Model for Combined Assessment of Monaural and Binaural Audio Quality. J Audio Eng Soc. 2024;72(9):536-551. doi:10.17743/jaes.2022.0161

@article{Eurich2024_22707,
  author = {Eurich, Bernhard and Ewert, Stephan D. and Dietz, Mathias and Biberger, Thomas},
  title = {{A Computationally Efficient Model for Combined Assessment of Monaural and Binaural Audio Quality}},
  journal = {Journal of the Audio Engineering Society},
  volume = {72},
  number = {9},
  pages = {536--551},
  year = {2024},
  month = sep,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2022.0161},
  url = {https://doi.org/10.17743/jaes.2022.0161}
}

TY  - JOUR
TI  - A Computationally Efficient Model for Combined Assessment of Monaural and Binaural Audio Quality
AU  - Eurich, Bernhard
AU  - Ewert, Stephan D.
AU  - Dietz, Mathias
AU  - Biberger, Thomas
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 72
IS  - 9
SP  - 536
EP  - 551
PY  - 2024
DA  - 2024/09/16
DO  - 10.17743/jaes.2022.0161
UR  - https://doi.org/10.17743/jaes.2022.0161
PB  - Audio Engineering Society
LA  - en
AB  - Audio quality is an important aspect of hearing aids, hearables, and sound reproduction systems because the signal processing of such devices might alter the spectral composition or interaural differences of the original sound and thus might degrade the perceived audio quality. Consequently, an audio quality model applicable to such devices requires accounting for monaural and binaural aspects of audio quality. Fleßner et al. successfully predicted overall audio quality by combining a monaural and binaural audio quality model, which is computationally expensive and thus limits the scope of application. In order to also cover time critical applications, such as real-time control of algorithms in audio and hearing technology, the authors present a computationally efficient model for overall audio quality in listeners with normal hearing. The suggested model was evaluated with six databases including quality ratings for music and speech signals processed by loudspeakers and algorithms typically applied in modern hearing devices (e.g., acoustic transparency, feedback cancellation or binaural beamforming). The presented model achieved a high prediction performance, indicated by the mean Pearson correlation of 0.9 similar to the more complex model of Fleßner et al., while its calculation time is substantially lower.
ER  -