Tony Orme began his engineering career at the BBC, London, where he trained as a broadcast engineer and successfully completed the network’s intensive three-year training program. While the BBC gave Tony a fantastic grounding in engineering, moving to independent television and satellite broadcasters, including Sky and ITV, gave him a deep insight into media production and workflows. With the added experience of working in network News operations, he witnessed firsthand the demands live television places on systems and people.
Dedicated to making things happen, Tony has a passion for improving systems, making them more efficient and getting them to work better. Throughout his career, Tony designed and built interface and processing solutions to address unique problems, ones that others were unable to resolve, especially as digital television moved to MPEG compression. He gained skills in low-level embedded RTOS C/C++ programming and FPGA-VHDL, using those tools to create high-speed video and audio real-time processing solutions for broadcasters.
Based on the knowledge gained during his Research and Development years, Tony now applies these skills to help others successfully migrate their facilities from SDI-based to IP-focused media centres.
As well as being a prolific writer for The Broadcast Bridge and lecturer at the University of Surrey, Tony is currently studying part time for his PhD.
The subject of Tony’s PhD research is “IP Timing Anomaly Detection Using Machine Learning” and covers two parallel emerging broadcasting technologies – IP and AI. Timing anomaly detection is achieved through IP long flow detection based on machine learning using LSTMs, a form of RNN with memory, and is particularly important when reducing latency while simultaneously increasing data throughput. This provides efficient flow trend detection so IP routers can achieve optimal switching strategies.