Data Science Education: The Signal Processing Perspective [SP Education]

Sharon Gannot, Zheng-Hua Tan, Martin Haardt, Nancy F. Chen, Hoi-To Wai, Ivan Tashev, Walter Kellermann, Justin Dauwels

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.