In this work the aircraft class recognition of based on take-off noise patterns is examined. Signal segmentation in time is analyzed as well as using a MLP neural network as the classifier for each segment. Also, several algorithms for decision by committee in order to aggregate the multiple parallel outputs of the classifiers are examined along with feature extraction and selection based on spectrum analysis of the aircraft noise. Also, a method for georeferenced estimation of the take-off flight path based only on the noise signal is explored. The methodology and results are sustained in the current literature.
Sánchez Pérez, L. A., Sánchez-Fernández, L. P., & Suarez-Guerra, S. (2016). Aircraft class recognition based on take-off noise patterns. Computacion y Sistemas, 799-825. https://doi.org/10.13053/CyS-20-4-2429