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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.
Author (s): O`Dwyer, Hugh; Csadi, Sebastian; Bates, Enda; Boland, Francis M.
Affiliation:
Trinity College, Dublin, Ireland
(See document for exact affiliation information.)
AES Convention: 146
Paper Number:509
Publication Date:
2019-03-06
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Session subject:
E-Brief Poster Session 2
Permalink: https://aes2.org/publications/elibrary-page/?id=20367
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O`Dwyer, Hugh; Csadi, Sebastian; Bates, Enda; Boland, Francis M.; 2019; A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance [PDF]; Trinity College, Dublin, Ireland; Paper 509; Available from: https://aes2.org/publications/elibrary-page/?id=20367
O`Dwyer, Hugh; Csadi, Sebastian; Bates, Enda; Boland, Francis M.; A Study in Machine Learning Applications for Sound Source Localization with Regards to Distance [PDF]; Trinity College, Dublin, Ireland; Paper 509; 2019 Available: https://aes2.org/publications/elibrary-page/?id=20367
@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},
number={509},
month={march},}