Comparison of Spatialization Techniques with Different Music Genres II

Aswathanarayana S. Comparison of Spatialization Techniques with Different Music Genres II. In: AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020). Audio Engineering Society; 2020. Paper 10504. Available from: https://aes.org/publications/elibrary-page/?id=21181

@inproceedings{Aswathanarayana2020_21181,
  author = {Aswathanarayana, Shashank},
  title = {{Comparison of Spatialization Techniques with Different Music Genres II}},
  booktitle = {AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)},
  note = {Paper 10504},
  year = {2020},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21181}
}

TY  - CPAPER
TI  - Comparison of Spatialization Techniques with Different Music Genres II
AU  - Aswathanarayana, Shashank
T2  - AES Conference: 2020 AES International Conference on Audio for Virtual and Augmented Reality (August 2020)
M1  - Paper 10504
PY  - 2020
DA  - 2020/08/06
UR  - https://aes.org/publications/elibrary-page/?id=21181
PB  - Audio Engineering Society
LA  - en
AB  - The main characteristics of a good spatialization algorithm are that the resulting sound image is enveloping, stable, clear. Such a sound image would then not only give a good sense of immersion to the listener but will also enable the listener to clearly localize the different sound elements, and ideally move around or at least have freedom to move the head and not have any major distortions in the listening experience. Part 1 of this study compared three algorithms, Ambisonics, VBAP and DBAP with four different music genres. In part 2, a more in-depth analysis is done of the results found in part 1. Binaural recordings done using a Neumann KU100 dummy head are used to compare the results found in the loudspeaker study with attributes found in the recordings. It was seen how more bright, next spectrally complex and more spectrally flat signals could have resulted in the user ratings.
ER  -