The System for Storing and Analyzing FM Broadcasting Content

This research presents a prototype system for the storage and analysis of broadcast content from FM radio stations, aimed at assisting the Office of The National Broadcasting and Telecommunications Commission (Office of the NBTC) in monitoring radio programs and advertisements to ensure compliance with laws and relevant regulations. The current content verification process faces limitations in terms of resources and coverage of signal reception. The proposed prototype system utilizes Raspberry Pi 4 Model B with Software Defined Radio (SDR) to receive and record FM radio signals. The recorded audio files are conversion to text using Speech-to-Text technology, followed by content analysis through Machine Learning techniques. This system can effectively detect and identify potentially illegal content, such as misleading advertisements or inappropriate material. Test results demonstrate that the developed system performs efficiently in content recording and offers higher accuracy in content analysis compared to traditional verification methods. Additionally, it helps reduce the workload of staff, cuts travel costs, and enhances the ability to monitor a broader range of content.