Data Science Education

The Signal Processing Perspective [SP Education]

Journal article (2023)

Authors

Sharon Gannot Bar-Ilan University
Zheng-Hua Tan Aalborg University
Martin Haardt Ilmenau University of Technology
Nancy F. Chen Institute for Infocomm Research
Hoi To Wai Chinese University of Hong Kong
Ivan Tashev Technical University of Sofia, University of Washington, Microsoft Research
Walter Kellermann Friedrich-Alexander-Universität Erlangen-Nürnberg
Justin Dauwels Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2023 Sharon Gannot, Zheng Hua Tan, Martin Haardt, Nancy F. Chen, Hoi To Wai, Ivan Tashev, Walter Kellermann, J.H.G. Dauwels
DOI: https://doi.org/10.1109/MSP.2023.3294709
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Published Date

2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

In the last decade, the signal processing (SP) community has witnessed a paradigm shift from model-based to data-driven methods. Machine learning (ML) - more specifically, deep learning - methodologies are nowadays widely used in all SP fields, e.g., audio, speech, image, video, multimedia, and multimodal/multisensor processing, to name a few. Many data-driven methods also incorporate domain knowledge to improve problem modeling, especially when computational burden, training data scarceness, and memory size are important constraints.

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