Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning
Miguel Sarabia Elena Menyaylenko Alessandro Toso Skyler Seto Zakaria Aldeneh Shadi Pirhosseinloo Luca Zappella Barry-John Theobald Nicholas Apostoloff Jonathan Sheaffer
We present Spatial LibriSpeech, a spatial audio dataset with over 650 hours
of 19-channel audio, first-order ambisonics, and optional distractor noise.
Spatial LibriSpeech is designed for machine learning model training, and it
includes labels for source position, speaking direction, room acoustics and
geometry. Spatial LibriSpeech is generated by augmenting LibriSpeech samples
with 200k+ simulated acoustic conditions across 8k+ synthetic rooms. To
demonstrate the utility of our dataset, we train models on four spatial audio
tasks, resulting in a median absolute error of 6.60{\deg} on 3D source
localization, 0.43m on distance, 90.66ms on T30, and 2.74dB on DRR estimation.
We show that the same models generalize well to widely-used evaluation
datasets, e.g., obtaining a median absolute error of 12.43{\deg} on 3D source
localization on TUT Sound Events 2018, and 157.32ms on T30 estimation on ACE
Challenge.