Towards Goal-Oriented Semantic Signal Processing: Applications and Future Challenges

Abstract

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.

44 pages, 29 figures, 3 tables. This preprint will be published in the Digital Signal Processing Journal's 30th anniversary issue on Future Signal Processing

References in corpus (7)

• Semantic Segmentation using Adversarial Networks
• Explain Images with Multimodal Recurrent Neural Networks
• Fast-SCNN: Fast Semantic Segmentation Network
• Wav2Letter: an End-to-End ConvNet-based Speech Recognition System
• W-Net: A Deep Model for Fully Unsupervised Image Segmentation
• Visual Relationship Detection using Scene Graphs: A Survey
• Attention-based Transducer for Online Speech Recognition