  Advances in machine learning technology have enabled real-time extraction of
semantic information in signals which can revolutionize signal processing
techniques and improve their performance significantly for the next generation
of applications. With the objective of a concrete representation and efficient
processing of the semantic information, we propose and demonstrate a formal
graph-based semantic language and a goal filtering method that enables
goal-oriented signal processing. The proposed semantic signal processing
framework can easily be tailored for specific applications and goals in a
diverse range of signal processing applications. To illustrate its wide range
of applicability, we investigate several use cases and provide details on how
the proposed goal-oriented semantic signal processing framework can be
customized. We also investigate and propose techniques for communications where
sensor data is semantically processed and semantic information is exchanged
across a sensor network.
