Data Science Education: The Signal Processing Perspective

Gannot S, Tan ZH, Haardt M, Chen NF, Wai HT, Tashev I, Kellermann W, Dauwels J (2023)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2023

Journal

IEEE Signal Processing Magazine Institute of Electrical and Electronic Engineers

Book Volume: 40

Pages Range: 89-93

Journal Issue: 7

URI: https://ieeexplore.ieee.org/document/10313218/

DOI: 10.1109/MSP.2023.3294709

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.

Authors with CRIS profile

Walter Kellermann Professur für Signalverarbeitung

Involved external institutions

Technische Universität Ilmenau

Germany (DE)

How to cite

APA:

Gannot, S., Tan, Z.-H., Haardt, M., Chen, N.F., Wai, H.-T., Tashev, I.,... Dauwels, J. (2023). Data Science Education: The Signal Processing Perspective. IEEE Signal Processing Magazine, 40(7), 89-93. https://dx.doi.org/10.1109/MSP.2023.3294709

MLA:

Gannot, S., et al. "Data Science Education: The Signal Processing Perspective." IEEE Signal Processing Magazine 40.7 (2023): 89-93.

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