Descriptive Analysis of Binaural Rendering with Virtual Loudspeakers Using a Rate-All-That-Apply Approach

Pike C, Melchior F, Tew A. Descriptive Analysis of Binaural Rendering with Virtual Loudspeakers Using a Rate-All-That-Apply Approach. In: AES Conference: 2016 AES International Conference on Headphone Technology. Audio Engineering Society; 2016. Paper 5-3. Available from: https://aes.org/publications/elibrary-page/?id=18348

@inproceedings{Pike2016_18348,
  author = {Pike, Chris and Melchior, Frank and Tew, Anthony},
  title = {{Descriptive Analysis of Binaural Rendering with Virtual Loudspeakers Using a Rate-All-That-Apply Approach}},
  booktitle = {AES Conference: 2016 AES International Conference on Headphone Technology},
  note = {Paper 5-3},
  year = {2016},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18348}
}

TY  - CPAPER
TI  - Descriptive Analysis of Binaural Rendering with Virtual Loudspeakers Using a Rate-All-That-Apply Approach
AU  - Pike, Chris
AU  - Melchior, Frank
AU  - Tew, Anthony
T2  - AES Conference: 2016 AES International Conference on Headphone Technology
M1  - Paper 5-3
PY  - 2016
DA  - 2016/08/06
UR  - https://aes.org/publications/elibrary-page/?id=18348
PB  - Audio Engineering Society
LA  - en
AB  - Spatial audio content for headphones is often created using binaural rendering of a virtual loudspeaker array. It is important to understand the effect of this choice on the sound quality. A sensory profiling evaluation was used to assess the perceived differences between direct binaural rendering and virtual loudspeaker rendering of a single sound source with and without head tracking and using anechoic and reverberant binaural impulse responses. A subset of the Spatial Audio Quality Inventory (SAQI) was used. Listeners first selected only attributes that they felt applied to the given stimuli. Initial analysis shows that tone colour and source direction are most affected by the use of this technique, but source extent, distance, and externalisation are also affected. Further work is required to analyse the sparse attribute rating data in depth.
ER  -