Reduce Energy Costs Thanks to AI WorldCast Systems: Artificial intelligence can help you align business and environmental needs
COMMENTARY By WorldCast Systems Cost reduction, more compact designs, more onboard features … these all reflect the current market trends in FM transmitter design. Energy savings is also increasingly a top-priority, especially when considering the Total Cost of Ownership of a system, and innovative companies are boosting the momentum with new solutions. Among these, a new innovation promises to transform the FM broadcasting landscape … this innovation is SmartFM.
WHAT IS SMARTFM?
SmartFM is our Artificial Intelligence (AI), which enables broadcasters to reduce their energy costs by up to 40%. HOW WAS THIS ARTIFICIAL INTELLIGENCE DEVELOPED?
With its Ecreso product line, WorldCast Systems has been in the RF business for over 60 years and has witnessed and participated in huge advancements in the design and performance of FM transmitters. However, much of this progress was achieved thanks to LDMOS technology which has now reached its maximum potential for improving transmitter efficiency. With this in mind, the company embarked on 3 years of intensive R&D, field testing and on-air measurements before bringing to market this new breakthrough technology. To understand the AI behind SmartFM, Nicolas Boulay, Co-President of WorldCast Systems explains: “The development of this Artificial Intelligence is made up of three phases: • Phase 1: a statistical approach for content breakdown and classification (this phase corresponds to the retrieval and analysis of large numbers of data — “our big data”). It is this phase which enabled us to create a library of audio content classified by their signal characteristics as well as by their impact on the acoustic experience Calculation based on a network of 10 FM transmitters over a 10year period. Comparison with “Old TX” at 55% efficiency, “Ecreso FM” at 74% overall efficiency, and “Ecreso FM” at 74% efficiency with SmartFM (green).
• Phase 2: a probabilistic approach in the implementation of content detection, Continued on page 14 ❱
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