Aircraft Noise Monitoring: Principles and Practice

2009 IEEE Conference on Emerging Technologies &Amp; Factory Automation

Ruiz

Pogrebnyak

2009

This work presents a computational model that allows the monitoring of aircraft generated noise. It makes spectral analysis and calculation of statistical indicators, as well as the aircrafts identification based on generated noise. This model also helps to foresee potential effects to health caused by this kind of noise during the aircraft takeoff, which is when the greatest impact are generated due to the sonorous levels that are reached. This model is implemented by means of software in a laptop, a data acquisition card and a calibrated sensor of acoustic pressure. The method can be included in a permanent monitoring system. The data acquisition is made at 25 KHz at 24 bits. The identification of the aircraft noise is done through two parallel neural networks combined with a weighted addition. In order to generate the inputs to the neural networks, parameters that were obtained from the auto-regressive model and the 1/12 octave analysis are used. This system has 13 categories of aircrafts and it has an identification level of 80% in real environments.

“…This filter was created to model the response of the human ear to sounds of great intensity. [3]. The calculation of the frequency weighting filter C values is based on Eq.…”

Section: A Pre-processingmentioning

confidence: 99%

Noise monitoring of aircrafts taking off based on neural model

2009 IEEE Conference on Emerging Technologies &Amp; Factory Automation

Ruiz

Pogrebnyak

2009

“…This filter was created to model the response of the human ear to sounds of great intensity. It is used to evaluate environmental sounds and low frequency sounds in the audible frequencies band [3]. The calculation of the frequency weighting filter C values is based on Eq.…”

Section: Pre-processingmentioning

confidence: 99%

Computational Model for Aircraft’s Takeoffs Pattern Recognition

Ruiz

Lecture Notes in Computer Science

Felipe-Riveron

et al. 2008

Abstract. This paper presents a novel computational multimodal model designed for pattern recognition of aircrafts' noise in real environments; with an 88.5% of effectiveness, it considers 13 different categories of aircrafts. This method includes measurements of signals of the noise produced during the takeoff at 25,000 samples per second and with a resolution of 24 bits, an spectral analysis made by means of an autoregressive model, an octave analysis, a normalization method created specifically for this work and two feed-forward neural networks. All the signals used for the design and evaluation of the results were obtained by means of field measurements.

“…The first group includes the components of uncertainty associated with the measurement of aircraft noise at a particular monitoring location. The second group includes the components of uncertainty associated with any subsequent data analysis that may be carried out [2], [3], [4]. The overall accuracy of any type of measurement is limited by various sources of error or uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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

Lecture Notes in Computer Science

Pogrebnyak

Rodríguez

et al.

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.