Multichannel-based Learning for Audio Object Extraction

@article{Arteaga2021MultichannelbasedLF,
  title={Multichannel-based Learning for Audio Object Extraction},
  author={Daniel Arteaga and Jordi Pons},
  journal={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2021},
  pages={206-210}
}
  • D. Arteaga, Jordi Pons
  • Published 11 February 2021
  • Computer Science
  • ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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… 

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