TY - JOUR
T1 - Airport take-off noise assessment aimed at identify responsible aircraft classes
AU - Sanchez-Perez, Luis A.
AU - Sanchez-Fernandez, Luis P.
AU - Shaout, Adnan
AU - Suarez-Guerra, Sergio
N1 - Publisher Copyright:
© 2015 Elsevier B.V..
PY - 2016/1/15
Y1 - 2016/1/15
N2 - Assessment of aircraft noise is an important task of nowadays airports in order to fight environmental noise pollution given the recent discoveries on the exposure negative effects on human health. Noise monitoring and estimation around airports mostly use aircraft noise signals only for computing statistical indicators and depends on additional data sources so as to determine required inputs such as the aircraft class responsible for noise pollution. In this sense, the noise monitoring and estimation systems have been tried to improve by creating methods for obtaining more information from aircraft noise signals, especially real-time aircraft class recognition. Consequently, this paper proposes a multilayer neural-fuzzy model for aircraft class recognition based on take-off noise signal segmentation. It uses a fuzzy inference system to build a final response for each class p based on the aggregation of K parallel neural networks outputs Opk with respect to Linear Predictive Coding (LPC) features extracted from K adjacent signal segments. Based on extensive experiments over two databases with real-time take-off noise measurements, the proposed model performs better than other methods in literature, particularly when aircraft classes are strongly correlated to each other. A new strictly cross-checked database is introduced including more complex classes and real-time take-off noise measurements from modern aircrafts. The new model is at least 5% more accurate with respect to previous database and successfully classifies 87% of measurements in the new database.
AB - Assessment of aircraft noise is an important task of nowadays airports in order to fight environmental noise pollution given the recent discoveries on the exposure negative effects on human health. Noise monitoring and estimation around airports mostly use aircraft noise signals only for computing statistical indicators and depends on additional data sources so as to determine required inputs such as the aircraft class responsible for noise pollution. In this sense, the noise monitoring and estimation systems have been tried to improve by creating methods for obtaining more information from aircraft noise signals, especially real-time aircraft class recognition. Consequently, this paper proposes a multilayer neural-fuzzy model for aircraft class recognition based on take-off noise signal segmentation. It uses a fuzzy inference system to build a final response for each class p based on the aggregation of K parallel neural networks outputs Opk with respect to Linear Predictive Coding (LPC) features extracted from K adjacent signal segments. Based on extensive experiments over two databases with real-time take-off noise measurements, the proposed model performs better than other methods in literature, particularly when aircraft classes are strongly correlated to each other. A new strictly cross-checked database is introduced including more complex classes and real-time take-off noise measurements from modern aircrafts. The new model is at least 5% more accurate with respect to previous database and successfully classifies 87% of measurements in the new database.
KW - Aircraft class
KW - Airport noise assessment
KW - Neural network
KW - Pattern recognition
KW - Signal segmentation
KW - Take-off noise
UR - http://www.scopus.com/inward/record.url?scp=84945957984&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2015.10.037
DO - 10.1016/j.scitotenv.2015.10.037
M3 - Artículo
C2 - 26540603
SN - 0048-9697
VL - 542
SP - 562
EP - 577
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 18542
ER -