Name
Abstract | ||
|---|---|---|
This paper introduces a new paradigm for sound source localization referred to as virtual acoustic space traveling (VAST) and presents a first dataset designed for this purpose. Existing sound source localization methods are either based on an approximate physical model (physics-driven) or on a specific-purpose calibration set (data-driven). With VAST, the idea is to learn a mapping from audio features to desired audio properties using a massive dataset of simulated room impulse responses. This virtual dataset is designed to be maximally representative of the potential audio scenes that the considered system may be evolving in, while remaining reasonably compact. We show that virtuallylearned mappings on this dataset generalize to real data, overcoming some intrinsic limitations of traditional binaural sound localization methods based on time differences of arrival. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-53547-0_7 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Sound localization,Binaural hearing,Room simulation,Machine learning | Conference | 10169 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.40 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clément Gaultier | 1 | 2 | 1.41 |
| Saurabh Kataria | 2 | 9 | 5.21 |
| Antoine Deleforge | 3 | 165 | 16.05 |