We continue our series on things to consider when designing broadcast audio systems with the beating heart of all things audio – the mixing console.
Remote Integration Model (REMI) production is more than remote cameras. It’s a new way of thinking and working. This tale of trying to implement a REMI production model within tight financial constraints highlights some of the operational challenges involved.
In part one of this series, we looked at why machine learning, with particular emphasis on neural networks, is different to traditional methods of statistical based classification and prediction. In this article, we investigate some of the applications specific for broadcasters that ML excels at.
The concept of leveraging the public Internet as the main infrastructure for widespread distribution of content, both for B2B and Direct-to-Consumer applications, is new to traditional broadcasters that have treated the digital side as a secondary silo to their linear channels, but that’s changing.
For a serious discussion about “making streaming broadcast-grade” we must address latency. Our benchmark is 5-seconds from encoder input to device, but we cannot compromise on quality. It’s not easy, and leaders in the field grapple with the trade-offs to ensure the best possible customer experience.