Binaural rendering of audio signals using waveform-based acoustic panning

Nishiguchi M, Mizutani Y, Watanabe K, Abe K, Ishikawa T, Enomoto S. Binaural rendering of audio signals using waveform-based acoustic panning. In: AES Convention 153. Audio Engineering Society; 2022. Paper 10627. Available from: https://aes.org/publications/elibrary-page/?id=21956

@inproceedings{Nishiguchi2022_21956,
  author = {Nishiguchi, Masayuki and Mizutani, Yuki and Watanabe, Kanji and Abe, Koji and Ishikawa, Tomokazu and Enomoto, Seigo},
  title = {{Binaural rendering of audio signals using waveform-based acoustic panning}},
  booktitle = {AES Convention 153},
  note = {Paper 10627},
  year = {2022},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=21956}
}

TY  - CPAPER
TI  - Binaural rendering of audio signals using waveform-based acoustic panning
AU  - Nishiguchi, Masayuki
AU  - Mizutani, Yuki
AU  - Watanabe, Kanji
AU  - Abe, Koji
AU  - Ishikawa, Tomokazu
AU  - Enomoto, Seigo
T2  - AES Convention 153
M1  - Paper 10627
PY  - 2022
DA  - 2022/10/06
UR  - https://aes.org/publications/elibrary-page/?id=21956
PB  - Audio Engineering Society
LA  - en
AB  - This study proposes computationally efficient binaural rendering for three-dimensional audio spaces. Instead of directly and individually convoluting each head-related impulse response (HRIR) within each sound object’s direction, only HRIRs of pre-defined representative directions are convoluted to the signals which are panned and summed up at representative positions. The panning is conducted adopting appropriate time shifts and gain adjustments to the original source signals, which approximately maintains waveforms of the source signals in synthesized sound images. We compared the obtained subjective and objective reproduction qualities with various representative HRIR direction arrangements using audio objects on a horizontal plane. Investigations indicated that the azimuth differences between representative HRIRs could be around 60?, and we then expect a reduction in computational complexity by approximately 50%–70% when 20–200 audio objects are rendered.
ER  -