Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning

Saurabh Kataria 1 Clément Gaultier 2 Antoine Deleforge 2
1 IIT Kanpur - Indian Institute of Technology Kanpur
2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper introduces a new framework for supervised sound source localization referred to as virtually-supervised learning. An acoustic shoe-box room simulator is used to generate a large number of binaural single-source audio scenes. These scenes are used to build a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls' absorption coefficients. A probabilistic high- to low-dimensional regression framework is used to learn a mapping from these features to the acoustic properties. Results indicate that this mapping successfully estimates the azimuth and elevation of new sources, but also their range and even the walls' absorption coefficients solely based on binaural signals. Results also reveal that incorporating random-diffusion effects in the data significantly improves the estimation of all parameters.
Keywords : Acoustic Modeling Sound source localization Machine Learning
Type de document :
Communication dans un congrès
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New-Orleans, United States. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017
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https://hal.inria.fr/hal-01372435
Contributeur : Antoine Deleforge <>
Soumis le : lundi 13 mars 2017 - 17:00:42
Dernière modification le : jeudi 15 novembre 2018 - 11:59:00
Document(s) archivé(s) le : mercredi 14 juin 2017 - 15:03:33

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  • HAL Id : hal-01372435, version 2
  • ARXIV : 1609.09747

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Saurabh Kataria, Clément Gaultier, Antoine Deleforge. Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning . 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Mar 2017, New-Orleans, United States. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017. 〈hal-01372435v2〉

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