Data-Driven Modeling of the Spatial Sound Experience

Härmä A, Park M, Kohlrausch A. Data-Driven Modeling of the Spatial Sound Experience. In: AES Convention 136. Audio Engineering Society; 2014. Paper 9025. Available from: https://aes.org/publications/elibrary-page/?id=17172

@inproceedings{Harma2014_17172,
  author = {Härmä, Aki and Park, Munhum and Kohlrausch, Armin},
  title = {{Data-Driven Modeling of the Spatial Sound Experience}},
  booktitle = {AES Convention 136},
  note = {Paper 9025},
  year = {2014},
  month = apr,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=17172}
}

TY  - CPAPER
TI  - Data-Driven Modeling of the Spatial Sound Experience
AU  - Härmä, Aki
AU  - Park, Munhum
AU  - Kohlrausch, Armin
T2  - AES Convention 136
M1  - Paper 9025
PY  - 2014
DA  - 2014/04/06
UR  - https://aes.org/publications/elibrary-page/?id=17172
PB  - Audio Engineering Society
LA  - en
AB  - Since the evaluation of audio systems or processing schemes is time-consuming and resource-expensive, alternative objective evaluation methods attracted considerable research interests. However, current perceptual models are not yet capable of replacing a human listener especially when the test stimulus is complex, for example, a sound scene consisting of time-varying multiple acoustic images. This paper describes a data-driven approach to develop a model to predict the subjective evaluation of complex acoustic scenes, where the extensive set of listening test results collected in the latest MPEG-H 3-D audio initiative was used as training data. The results showed that a few selected outputs of various auditory models may be a useful set of features, where linear regression and multilayer perceptron models reasonably predicted the overall distribution of listening test scores, estimating both mean and variance.
ER  -