VAST : The Virtual Acoustic Space Traveler Dataset - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

VAST : The Virtual Acoustic Space Traveler Dataset

Abstract

This paper introduces a new paradigm for sound source lo-calization 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 virtually-learned mappings on this dataset generalize to real data, overcoming some intrinsic limitations of traditional binaural sound localization methods based on time differences of arrival.
Fichier principal
Vignette du fichier
main_lva2017_gaultier.pdf (824.5 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01416508 , version 1 (14-12-2016)

Identifiers

Cite

Clément Gaultier, Saurabh Kataria, Antoine Deleforge. VAST : The Virtual Acoustic Space Traveler Dataset. International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA), Feb 2017, Grenoble, France. ⟨hal-01416508⟩
825 View
358 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More