VAST : The Virtual Acoustic Space Traveler Dataset
This addresses sound source localization for audio processing applications, representing a new paradigm rather than an incremental improvement.
The paper tackles sound source localization by introducing the Virtual Acoustic Space Traveler (VAST) dataset, which uses simulated room impulse responses to learn mappings that generalize to real data, overcoming limitations of traditional methods like those based on time differences of arrival.
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.