TY - JOUR
T1 - Aircraft class identification based on take-off noise signal segmentation in time
AU - Sánchez-Pérez, Luis Alejandro
AU - Sánchez-Fernández, Luis Pastor
AU - Suárez-Guerra, Sergio
AU - Carbajal-Hernández, José Juan
PY - 2013
Y1 - 2013
N2 - Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the aircraft type as the key element. This paper presents a new computational model to identify the aircraft class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The aircraft are grouped into classes depending on the installed engine type. The model works with 13 aircraft categories with an identification level above 85% in real environments.
AB - Aircraft noise is one of the most uncomfortable kinds of sounds. That is why many organizations have addressed this problem through noise contours around airports, for which they use the aircraft type as the key element. This paper presents a new computational model to identify the aircraft class with a better performance, because it introduces the take-off noise signal segmentation in time. A method for signal segmentation into four segments was created. The aircraft noise patterns are extracted using an LPC (Linear Predictive Coding) based technique and the classification is made combining the output of four parallel MLP (Multilayer Perceptron) neural networks, one for each segment. The individual accuracy of each network was improved using a wrapper feature selection method, increasing the model effectiveness with a lower computational cost. The aircraft are grouped into classes depending on the installed engine type. The model works with 13 aircraft categories with an identification level above 85% in real environments.
KW - Acoustic
KW - Aircraft
KW - Classification
KW - Noise
KW - Signal segmentation
KW - Take-off
UR - http://www.scopus.com/inward/record.url?scp=84878302290&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2013.03.017
DO - 10.1016/j.eswa.2013.03.017
M3 - Artículo
SN - 0957-4174
VL - 40
SP - 5148
EP - 5159
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 13
ER -