Integrating IP broadcasting with audio tags: Workflow and challenges

Burchett-Vass R, Singh A, Bibbó G, Plumbley MD. Integrating IP broadcasting with audio tags: Workflow and challenges. In: 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio. Audio Engineering Society; 2025. Paper 29. Available from: https://aes.org/publications/elibrary-page/?id=23018

@inproceedings{BurchettVass2025_23018,
  author = {Burchett-Vass, Rhys and Singh, Arshdeep and Bibbó, Gabriel and Plumbley, Mark D.},
  title = {{Integrating IP broadcasting with audio tags: Workflow and challenges}},
  booktitle = {2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio},
  note = {Paper 29},
  year = {2025},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=23018}
}

TY  - CPAPER
TI  - Integrating IP broadcasting with audio tags: Workflow and challenges
AU  - Burchett-Vass, Rhys
AU  - Singh, Arshdeep
AU  - Bibbó, Gabriel
AU  - Plumbley, Mark D.
T2  - 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio
M1  - Paper 29
PY  - 2025
DA  - 2025/09/02
UR  - https://aes.org/publications/elibrary-page/?id=23018
PB  - Audio Engineering Society
LA  - en
AB  - The broadcasting industry is increasingly adopting IP technologies, revolutionising both live and pre-recorded content production, from news gathering to live music events. IP broadcasting allows for the transport of audio and video signals in an easily configurable way, aligning with modern networking techniques. This shift towards an IP workflow allows for much greater flexibility, not only in routing signals but with the integration of tools using standard web development techniques. One possible tool could include the use of live audio tagging, which has a number of uses in the production of content. These could include adding sound effects to automated closed captioning or identifying unwanted sound events within a scene. In this paper, we describe the process of containerising an audio tagging model into a microservice, a small segregated code module that can be integrated into a multitude of different network setups. The goal is to develop a modular, accessible, and flexible tool capable of seamless deployment into broadcasting workflows of all sizes, from small productions to large corporations. Challenges surrounding latency of the selected audio tagging model and its effect on the usefulness of the end product are discussed.
ER  -