NRDT: A transform with only 16 code lines, proved so far a convenient substitute for more sophisticated transforms (e.g. MFCCs) in various signal recognition problems. No multipliers, simple algorithm, one can apply it for various signals by optimzing the delays in the algorithm.
Particularly useful for HW-oriented devices (MCU, FPGAs) in the Tiny-ML context
Copyright Radu DOGARU radu.dogaru@upb.ro Last update 20 July 2025
It represents a revised and simplified code replacement for the older RDT Python code provided in https://github.com/radu-dogaru/NL-CNN-RDT-based-sound-classification-
For facile access to datasets in examples, it is prefferable to run the notebook on Kaggle
Relevant papers (please cite)
[1] R. Dogaru and I. Dogaru, "A low complexity solution for epilepsy detection using an improved version of the reaction-diffusion transform," 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE), Galati, Romania, 2017, pp. 1-6, doi: 10.1109/ISEEE.2017.8170678. https://ieeexplore.ieee.org/document/8170678
[2] R. Dogaru and I. Dogaru, "State of the Art Recognition of Emotions from Speech, Using a Low Complexity Solution Based on Reaction-Diffusion Transform," 2022 International Symposium on Electronics and Telecommunications (ISETC), Timisoara, Romania, 2022, pp. 1-4, doi: 10.1109/ISETC56213.2022.10010234. https://ieeexplore.ieee.org/document/10010234
History: The RDT idea emerged in 2000's inspired from cellular-automata works in the context of finding some conveninet measure to quantify numerically emergent behaviors. See more in: https://ieeexplore.ieee.org/document/1630267
Later, in 2007 we first expanded it succesfully (introducing the idea of sub-sampling in some m-channels) to sound recognition problems. See more in: https://ieeexplore.ieee.org/document/4410603
Since then it proved a convenient feature for either audio or bio-medical signals, competing well against traditional spectrogram algorithms. See more here: https://ieeexplore.ieee.org/search/searchresult.jsp?action=search&newsearch=true&matchBoolean=true&queryText=(%22Full%20Text%20Only%22:%22reaction-diffusion%20transform%22)%20AND%20(%22Authors%22:Dogaru)