Noise Pattern Recognition of Airplanes Taking Off: Task for a Monitoring System

Abstract

This paper presents an original work for aircraft noise monitoring systems and it analyzes the airplanes noise signals and a method to identify them. The method uses processed spectral patterns and a neuronal network feed-forward, programmed by means of virtual instruments. The obtained results, very useful in portable systems, make possible to introduce redundancy to permanent monitoring systems. The noise level in a city has fluctuations between 50 dB (A) and 100 dB (A). It depends on the population density and its activity, commerce and services in the public thoroughfare, terrestrial and aerial urban traffic, of the typical activities of labor facilities and used machinery, which give varied conditions that must be faced of diverse ways within the corresponding normalization. The sounds or noises that exceed the permissible limits, whichever the activities or causes that originate them, are considered events susceptible to degrade the environment and the health.

Convolutional Neural Networks for Aircraft Noise Monitoring

“…Similarly, in [21] the MFCC coefficients were computed along with some hand-selected features on the 1/24 octave data for each event and fed both to separate feed-forward Artificial Neural Networks (ANNs) for classification. In [8] the authors trained an ANN on the spectral forms (after some smoothing) of 24 second windows containing noise events to perform this classification.…”

Military aircrafts’ classification based on their sound signature

“…Measuring the noise level that airplanes produce, as well as recognizing them from their noise pattern, has attracted researchers interested in environmental issues (Fernandez et al, 2007;Márquez-Molina et al, 2014;Barbo et al, 2009). Another important application of signal analysis related to airplanes includes the non-destructive testing for the detection of cracks in the aircraft's frame (Kappatos and Dermatas, 2007).…”

“…Research work on aircraft acoustic classification is mainly developed for noise monitoring and for airport traffic monitoring purposes (Asensio et al, 2010;Brooks and DeMetz, 1996;Fernandez et al, 2007). As mentioned, both military and commercial aircrafts can be classified using radars or according to recent advances in research, based on their motions' characteristics (Golmohammad et al, 2006) and on the communication signals they transmit (Feng et al, 2008).…”

Unattended acoustic events classification at the vicinity of airports

“…Several recent literature studies used the noise pattern recognition for the identification of aircraft takeoffs [11,12]; results evidenced that approximately 90% of events are correctly classified using a multimodal autoregressive model. A real time method for the identification of the aircraft's sounds has been proposed in Ref.…”