Abstract
This paper presents a novel computational multimodal model designed for pattern recognition of aircrafts’ noise in real environments; with an 88.5% of effectiveness, it considers 13 different categories of aircrafts. This method includes measurements of signals of the noise produced during the takeoff at 25,000 samples per second and with a resolution of 24 bits, an spectral analysis made by means of an autoregressive model, an octave analysis, a normalization method created specifically for this work and two feed-forward neural networks. All the signals used for the design and evaluation of the results were obtained by means of field measurements.
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Rojo Ruiz, A., Sánchez Fernandez, L.P., Felipe-Riverón, E., Suárez Guerra, S. (2008). Computational Model for Aircraft’s Takeoffs Pattern Recognition. In: Ruiz-Shulcloper, J., Kropatsch, W.G. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2008. Lecture Notes in Computer Science, vol 5197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85920-8_2
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DOI: https://doi.org/10.1007/978-3-540-85920-8_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85919-2
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