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Integrating IP Broadcasting with Audio Tags: Workflow and Challenges

arXiv 2407.15423 ↗First posted 22 Jul 2024eess.AS cs.AI cs.MMcited by 0

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Abstract (arXiv)

The broadcasting industry has adopted 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 21 references that are in the map. First posted on arXiv 22 Jul 2024.

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5 of 21 references.

References · 5

  1. 2021AST: Audio Spectrogram Transformercited by 634
  2. 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 563
  3. 2019Deep Learning for Audio Signal Processingcited by 92
  4. 2022Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillationcited by 25
  5. 2023E-PANNs: Sound Recognition Using Efficient Pre-trained Audio Neural Networkscited by 4

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