| Resumen |
The acoustic impact of aircraft taking-off is an important subject for monitoring and research. It is very useful to analyze the type or class of aircraft that produces high level noises based on take-off characteristics. This paper presents a new method about aircraft classification and the acoustic impact estimation, in areas near an airport, based on real time noise measurement for each take-off. The noise measurements are made with sampling frequency of 50 ks/s (kilo samples per second) and 24-bit resolution analog-to-digital conversion, during 24 s. The aircraft identification is made through a model of two parallel feed-forward neural network combined with a weighted addition. In order to generate the inputs to the neural networks, the noise signal features were obtained from the auto-regressive model and the 1/12 octave analysis. The aircraft is grouped into categories or classes depending on the installed engine type. This system has 13 aircraft categories and an identification level above 80% in real environments. Noise signals, generated during aircraft take-off are measured in a fixed location on the airport runway end using a linear 4-microphone array. The acoustic impact is presented by means of a noise map for each take-off and displays four layers related to four take-off time intervals. Based on International Organization for Standardization, each time interval is represented by an equivalent point sound source location through the estimation of time-differenc |
