Abstract
Deep learning has proven itself to be a powerful tool to develop datadriven signal processing algorithms for challenging engineering problems. By learning the key features and characteristics of the input signals instead of requiring a human to first identify and model them, learned algorithms can beat many human-made algorithms. In particular, deep neural networks are capable of learning the complicated features of nature-made signals, such as photos and audio recordings, and using them for classification and decision making.
Original language | English |
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Article number | 9186132 |
Pages (from-to) | 134-140 |
Number of pages | 7 |
Journal | IEEE Signal Processing Magazine |
Volume | 37 |
Issue number | 5 |
DOIs | |
Publication status | Published - 2020 |
Subject classification (UKÄ)
- Computer Vision and Robotics (Autonomous Systems)