AES E-Library

H. O'Dwyer, S. Csadi, E. Bates, and FR. M.. Boland, "A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance," Engineering Brief 509, (2019 March.). doi: H. O'Dwyer, S. Csadi, E. Bates, and FR. M.. Boland, "A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance," Engineering Brief 509, (2019 March.). doi:
Abstract: 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.

@article{o'dwyer2019a,
author={o'dwyer, hugh and csadi, sebastian and bates, enda and boland, francis m.},
journal={journal of the audio engineering society},
title={a study in machine learning applications for sound source localization with regards to distance},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},} @article{o'dwyer2019a,
author={o'dwyer, hugh and csadi, sebastian and bates, enda and boland, francis m.},
journal={journal of the audio engineering society},
title={a study in machine learning applications for sound source localization with regards to distance},
year={2019},
volume={},
number={},
pages={},
doi={},
month={march},
abstract={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.},}

TY - paper
TI - A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance
SP - EP -
AU - O'Dwyer, Hugh
AU - Csadi, Sebastian
AU - Bates, Enda
AU - Boland, Francis M.
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019 TY - paper
TI - A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance
SP - EP -
AU - O'Dwyer, Hugh
AU - Csadi, Sebastian
AU - Bates, Enda
AU - Boland, Francis M.
PY - 2019
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - March 2019
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.