A Model to Predict the Impact of Dialog Enhancement or Mix Ratio on a Large Audience

Master A, Muesch H. A Model to Predict the Impact of Dialog Enhancement or Mix Ratio on a Large Audience. In: AES Convention 149. Audio Engineering Society; 2020. Paper 637. Available from: https://aes.org/publications/elibrary-page/?id=20923

@inproceedings{Master2020_20923,
  author = {Master, Aaron and Muesch, Hannes},
  title = {{A Model to Predict the Impact of Dialog Enhancement or Mix Ratio on a Large Audience}},
  booktitle = {AES Convention 149},
  note = {Paper 637},
  year = {2020},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20923}
}

TY  - CPAPER
TI  - A Model to Predict the Impact of Dialog Enhancement or Mix Ratio on a Large Audience
AU  - Master, Aaron
AU  - Muesch, Hannes
T2  - AES Convention 149
M1  - Paper 637
PY  - 2020
DA  - 2020/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20923
PB  - Audio Engineering Society
LA  - en
AB  - Increasing the speech-to-background mix ratio of content, either algorithmically through dialog enhancement (DE), or during production, is considered a means of reducing listening effort for an audience, some members of which have hearing impairments. But what exactly is the expected benefit? A portion of the audience can already follow the content effortlessly and dialog boosting will not improve their perception. Other parts of the audience are severely impaired, and their speech reception performance will improve until all background is removed. We introduce a model that predicts which parts of an audience benefit by how much from changing the speech-to-background mix ratio of a piece of content. The model is intended to allow decision makers to predict what impact changes in audio production guidelines or DE technologies will have on their audience.
ER  -