Multichannel-based learning for audio object extraction
This addresses scalability issues in audio object extraction for immersive content creation, though it is incremental as it builds on existing separation and estimation methods.
The paper tackles the challenge of extracting multiple simultaneous audio objects from multichannel mixes, proposing a deep learning approach that learns from renders instead of objects directly, achieving performance improvements over baselines in specific scenarios.
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.