Assessment and Prediction of Binaural Aspects of Audio Quality

Fleßner JH, Huber R, Ewert SD. Assessment and Prediction of Binaural Aspects of Audio Quality. J Audio Eng Soc. 2017;65(11):929-942. doi:10.17743/jaes.2017.0037

@article{Flesner2017_19361,
  author = {Fleßner, Jan-Hendrik and Huber, Rainer and Ewert, Stephan D.},
  title = {{Assessment and Prediction of Binaural Aspects of Audio Quality}},
  journal = {Journal of the Audio Engineering Society},
  volume = {65},
  number = {11},
  pages = {929--942},
  year = {2017},
  month = nov,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2017.0037},
  url = {https://doi.org/10.17743/jaes.2017.0037}
}

TY  - JOUR
TI  - Assessment and Prediction of Binaural Aspects of Audio Quality
AU  - Fleßner, Jan-Hendrik
AU  - Huber, Rainer
AU  - Ewert, Stephan D.
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 65
IS  - 11
SP  - 929
EP  - 942
PY  - 2017
DA  - 2017/11/06
DO  - 10.17743/jaes.2017.0037
UR  - https://doi.org/10.17743/jaes.2017.0037
PB  - Audio Engineering Society
LA  - en
AB  - Binaural or spatial presentation of audio signals has become increasingly important not only in consumer sound reproduction, but also for hearing-assistive devices like hearing aids, where signals in both ears might undergo extensive signal processing. Such processing may introduce distortions to the interaural signal properties that affect perception. In this research, an approach for intrusive binaural auditory-model-based quality prediction (BAM-Q) is introduced. BAM-Q uses a binaural auditory model as front-end to extract the three binaural features: interaural level difference, interaural time difference, and a measure of interaural coherence. The current approach focuses on the general applicability (with respect to binaural signal differences) of the binaural quality model to arbitrary binaural audio signals. Two listening experiments were conducted to subjectively measure the influence of these binaural features and their combinations on binaural quality perception. The results were used to train BAM-Q. Two different hearing aid algorithms were used to evaluate the performance of the model. The correlations between subjective mean ratings and model predictions are higher than 0.9.
ER  -