Dynamic hierarchical aggregation of parallel outputs for aircraft take-off noise identification

Sánchez-Pérez, Luis Alejandro; Sánchez-Fernández, Luis Pastor; Suárez-Guerra, Sergio; Lopez-Pacheco, Maria G.

doi:10.1016/j.engappai.2015.08.002

Cited by 7 publications

(8 citation statements)

References 13 publications

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“…They also suggest that the accuracy rate decreases with more aircraft models to be classified. Ruiz et al [9] and Sanchez-Perez et al [10,11] reported the results of using a neural network to classify 13 types of jet aircraft, the identification rate being 85-90%. As such, the accuracy of aircraft model identification in previous studies targeting jet aircraft is approximately 90%.…”

Section: Previous Studiesmentioning

confidence: 99%

Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Morinaga

Mori

Matsui

et al. 2019

Acoust. Sci. & Tech.

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We have been developing an aircraft model identification system that uses a convolutional neural network (CNN). The assumption is that this identification system would be used to estimate the number of flights to create noise maps. In our previous study, we used the CNN model to classify five aircraft comprising three rotorcraft, one turboprop, and one jet aircraft, and the accuracy reached 99%. In the present study, to examine whether this method is also effective for identifying the sound sources of jet aircraft, we conducted two case studies using frequency characteristics of aircraft noise obtained from field measurements around Osaka International Airport and Narita International Airport. Targeting 7 and 18 types of sound source at Osaka and Narita, respectively, an identification rate of 98% was obtained in both cases. This suggests that the present system can estimate the number of jet aircraft flights for each engine type or each aircraft model with very high accuracy.

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Section: Previous Studiesmentioning

confidence: 99%

Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Morinaga

Mori

Matsui

et al. 2019

Acoust. Sci. & Tech.

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“…En la Secciones 2 y 3, se utiliza una red neuronal para cada segmento (véase [19,31]) mientras que en [23] se usan dos redes paralelas para evaluar dos tipos diferentes de rasgos extraídos de la misma señal. En todos los casos se establece un algoritmo de agregación para construir la respuesta final a partir de las múltiples salidas producidas por todas las redes neuronales.…”

Section: Modelo Multicapa Neuro-difusounclassified

“…Más detalles de la metodología y resultados obtenidos en este trabajo pueden ser consultados en [19,22,23,31,35,38].…”

Section: Conclusionesunclassified

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Aircraft Class Recognition based on Take-off Noise Patterns

Pérez

Sánchez-Fernández²,

Guerra³

2016

CyS

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Resumen. En este trabajo se examina el reconocimiento de la clase de aeronaves a partir de patrones del ruido en el despegue. Se analiza la segmentación de la señal en tiempo y el uso de una red neuronal MLP por cada segmento. Asimismo, se examinan varios algoritmos de decisión por comité para la agregación de las múltiples salidas de los clasificadores paralelos, así como la extracción y selección de características con base en el análisis del espectro del ruido de aeronaves. Por otro lado, se explora un método para estimar la trayectoria georreferenciada durante el despegue únicamente a partir de la señal. La metodología y los resultados están sustentados en la literatura actual. Palabras clave. Ruido en aeropuertos, clasificación de aeronaves, segmentación de la señal, redes neuronales.

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“…Sanchez-Perez et al [14] introduced a new model for aircraft class recognition based on take-off noise signal segmentation and the paper [15] presents an adaptive noise cancelling (ANC) and time-frequency application for railway wheel flat and rail surface defect detection. These papers are the only ones using noise parameters for detecting types of airplanes or detection of possible failures in a vehicle structure.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Noise Monitoring in Function of Flight Safety and Aircraft Model Determination

Vidović

Štimac²,

Zečević-Tadić³

2017

Journal of Advanced Transportation

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This paper presents the research of noise level monitoring at the Zagreb Airport Ltd. The purpose of this paper is to show how the aircraft noise sources can be used as a tool for aircraft detection and for increasing flight safety. The noise measurement was made at the Zagreb Airport by using its professional noise monitoring system. The research has led to new findings, such as determining the aircraft model by measuring the noise level generated by an aircraft during final approach based on its frequency spectrum, as the connectivity from the airport’s side to the radar data was unavailable (only ATC). In addition, it is possible to determine the aircraft altitude and, perhaps most significantly, the increase of flight safety through the detection of potential failures on the aircraft structure and/or engine during the overflight of a noise monitoring terminal.

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Dynamic hierarchical aggregation of parallel outputs for aircraft take-off noise identification

Cited by 7 publications

References 13 publications

Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Identification of jet aircraft model based on frequency characteristics of noise by convolutional neural network

Aircraft Class Recognition based on Take-off Noise Patterns

Aircraft Noise Monitoring in Function of Flight Safety and Aircraft Model Determination

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