VAST : The Virtual Acoustic Space Traveler Dataset

Clément Gaultier (1) , Saurabh Kataria (2) , Antoine Deleforge (1)

1 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
2 IIT Kanpur - Indian Institute of Technology Kanpur

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.

Mots clés: Machine Learning; Binaural Hearing; Room Simulation; Sound Localization

Domaines: Apprentissage [cs.LG]; Son [cs.SD]

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⟩