@inproceedings{23afe2f09fa345b0be11df9be6a5eb84,
title = "Aircraft classification and noise map estimation based on real-time measurements of take-off noise",
abstract = "This paper summarizes a new methodology about aircrafts identification and the generation of estimated noise map based on real time noise measurement for each take-off. The data acquisition is made at 50 Ks/s and 24 bits, during 24 seconds of aircraft take-off. The aircraft identification is made through two parallel neural networks combined with a weighted addition. In order to generate the inputs to the neural networks, the features were obtained from the auto-regressive (AR) model and the 1/12 octave analysis. This system has 13 categories of aircrafts and has an identification level above 84% in real environments. Noise signals generated during aircraft take-off are measured in a fixed location on the airport runway end using a linear 4-microphone array. The noise map is made for each take-off and presents four layers related to four time intervals of take-off. Each time interval is represented by an equivalent point sound source location based on estimation of time-difference-of-arrival (TDOA) of the acoustic wave of aircraft taking-off.",
keywords = "Aircraft, Identification, Map, Noise, Real time, Sound",
author = "Fernandez, {Luis Pastor Sanchez} and {Sanchez Perez}, {Luis A.} and {Moreno Ibarra}, {Marco A.}",
year = "2011",
language = "Ingl{\'e}s",
isbn = "9789898425843",
series = "NCTA 2011 - Proceedings of the International Conference on Neural Computation Theory and Applications",
pages = "153--162",
booktitle = "NCTA 2011 - Proceedings of the International Conference on Neural Computation Theory and Applications",
note = "International Conference on Neural Computation Theory and Applications, NCTA 2011 ; Conference date: 24-10-2011 Through 26-10-2011",
}