  The current paradigm for creating and deploying immersive audio content is
based on audio objects, which are composed of an audio track and position
metadata. While rendering an object-based production into a multichannel mix is
straightforward, the reverse process involves sound source separation and
estimating the spatial trajectories of the extracted sources. Besides,
cinematic object-based productions are often composed by dozens of simultaneous
audio objects, which poses a scalability challenge for audio object extraction.
Here, we propose a novel deep learning approach to object extraction that
learns from the multichannel renders of object-based productions, instead of
directly learning from the audio objects themselves. This approach allows
tackling the object scalability challenge and also offers the possibility to
formulate the problem in a supervised or an unsupervised fashion. Since, to our
knowledge, no other works have previously addressed this topic, we first define
the task and propose an evaluation methodology, and then discuss under what
circumstances our methods outperform the proposed baselines.
