TY - JOUR
T1 - Data Science Education
T2 - The Signal Processing Perspective [SP Education]
AU - Gannot, Sharon
AU - Tan, Zheng Hua
AU - Haardt, Martin
AU - Chen, Nancy F.
AU - Wai, Hoi To
AU - Tashev, Ivan
AU - Kellermann, Walter
AU - Dauwels, Justin
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2023/11/1
Y1 - 2023/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85177218280&partnerID=8YFLogxK
U2 - 10.1109/MSP.2023.3294709
DO - 10.1109/MSP.2023.3294709
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AN - SCOPUS:85177218280
SN - 1053-5888
VL - 40
SP - 89
EP - 93
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 7
ER -