A tutorial review on entropy-based handcrafted feature extraction for information fusion

Abstract

Entropy (H) is the main subject of this article, concisely written to serve as a tutorial introducing two feature extraction (FE) methods for usage in digital signal processing (DSP) and pattern recognition (PR). The theory, carefully exposed, is supplemented with numerical cases, augmented with C/C++ source-codes and enriched with example applications on restricted-vocabulary speech recognition and image synthesis. Complementarily and as innovatively shown, the ordinary calculation of H corresponds to the outcome of a partially pre-tuned deep neural network architecture which fuses important information, bringing a cutting-edge point-of-view for both DSP and PR communities.