TY - JOUR
T1 - Aircraft classification and acoustic impact estimation based on real-time take-off noise measurements
AU - Sánchez Fernández, Luis P.
AU - Sánchez Pérez, Luis A.
AU - Carbajal Hernández, José J.
AU - Rojo Ruiz, Arturo
N1 - Funding Information:
Acknowledgments We wish to thank to the National Council on Science and Technology (CONACYT) and to the National Polytechnic Institute of Mexico, for their financial support for this research project.
PY - 2013/10
Y1 - 2013/10
N2 - The acoustic impact of aircraft taking-off is an important subject for monitoring and research. It is very useful to analyze the type or class of aircraft that produces high level noises based on take-off characteristics. This paper presents a new method about aircraft classification and the acoustic impact estimation, in areas near an airport, based on real time noise measurement for each take-off. The noise measurements are made with sampling frequency of 50 ks/s (kilo samples per second) and 24-bit resolution analog-to-digital conversion, during 24 s. The aircraft identification is made through a model of two parallel feed-forward neural network combined with a weighted addition. In order to generate the inputs to the neural networks, the noise signal features were obtained from the auto-regressive model and the 1/12 octave analysis. The aircraft is grouped into categories or classes depending on the installed engine type. This system has 13 aircraft categories and an identification level above 80% 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 acoustic impact is presented by means of a noise map for each take-off and displays four layers related to four take-off time intervals. Based on International Organization for Standardization, each time interval is represented by an equivalent point sound source location through the estimation of time-difference-of-arrival of the acoustic wave from aircraft taking-off.
AB - The acoustic impact of aircraft taking-off is an important subject for monitoring and research. It is very useful to analyze the type or class of aircraft that produces high level noises based on take-off characteristics. This paper presents a new method about aircraft classification and the acoustic impact estimation, in areas near an airport, based on real time noise measurement for each take-off. The noise measurements are made with sampling frequency of 50 ks/s (kilo samples per second) and 24-bit resolution analog-to-digital conversion, during 24 s. The aircraft identification is made through a model of two parallel feed-forward neural network combined with a weighted addition. In order to generate the inputs to the neural networks, the noise signal features were obtained from the auto-regressive model and the 1/12 octave analysis. The aircraft is grouped into categories or classes depending on the installed engine type. This system has 13 aircraft categories and an identification level above 80% 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 acoustic impact is presented by means of a noise map for each take-off and displays four layers related to four take-off time intervals. Based on International Organization for Standardization, each time interval is represented by an equivalent point sound source location through the estimation of time-difference-of-arrival of the acoustic wave from aircraft taking-off.
KW - Acoustic contamination
KW - Aircraft
KW - Measurement
KW - Recognition
KW - Take-off noise
UR - http://www.scopus.com/inward/record.url?scp=84885431476&partnerID=8YFLogxK
U2 - 10.1007/s11063-012-9258-5
DO - 10.1007/s11063-012-9258-5
M3 - Artículo
SN - 1370-4621
VL - 38
SP - 239
EP - 259
JO - Neural Processing Letters
JF - Neural Processing Letters
IS - 2
ER -