A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance

O'Dwyer H, Csadi S, Bates E, Boland FM. A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance. In: AES Convention 146. Audio Engineering Society; 2019. Paper 509. Available from: https://aes.org/publications/elibrary-page/?id=20367

@inproceedings{ODwyer2019_20367,
  author = {O'Dwyer, Hugh and Csadi, Sebastian and Bates, Enda and Boland, Francis M.},
  title = {{A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance}},
  booktitle = {AES Convention 146},
  note = {Paper 509},
  year = {2019},
  month = mar,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20367}
}

TY  - CPAPER
TI  - A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance
AU  - O'Dwyer, Hugh
AU  - Csadi, Sebastian
AU  - Bates, Enda
AU  - Boland, Francis M.
T2  - AES Convention 146
M1  - Paper 509
PY  - 2019
DA  - 2019/03/06
UR  - https://aes.org/publications/elibrary-page/?id=20367
PB  - Audio Engineering Society
LA  - en
AB  - This engineering brief outlines how Machine Learning (ML) can be used to estimate objective sound source distance by examining both the temporal and spectral content of binaural signals. A simple ML algorithm is presented that is capable of predicting source distance to within half a meter in a previously unseen environment. This algorithm is trained using a selection of features extracted from synthesized binaural speech. This enables us to determine which of a selection of cues can be best used to predict sound source distance in binaural audio. The research presented can be seen not only as an exercise in ML but also as a means of investigating how binaural hearing works.
ER  -