VAST : The Virtual Acoustic Space Traveler Dataset
Cl\'ement Gaultier (PANAMA), Saurabh Kataria (PANAMA, IIT Kanpur),, Antoine Deleforge (PANAMA)

TL;DR
This paper presents VAST, a large-scale virtual acoustic dataset for training sound source localization models, demonstrating that models trained on this dataset generalize well to real-world data and improve upon traditional methods.
Contribution
Introduction of the VAST dataset for virtual acoustic space traveling, enabling effective learning of sound localization mappings from simulated to real environments.
Findings
Models trained on VAST generalize to real data
VAST dataset improves sound localization accuracy
Overcomes limitations of traditional binaural localization methods
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.
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Taxonomy
TopicsSpeech and Audio Processing · Hearing Loss and Rehabilitation · Music and Audio Processing
