Discrete-time signal processing
Discrete-time signal processing
Speech Coding Algorithms: Foundation and Evolution of Standardized Coders
Speech Coding Algorithms: Foundation and Evolution of Standardized Coders
Noise pattern recognition of airplanes taking off: task for a monitoring system
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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The research is based on 10-node autonomous, distributed wireless monitoring system placed in various locations throughout Historical Center of Mexico City to take permanent measurements of the noise levels and stream the data back to the main monitoring station every five minutes and the measurements of signals of the noise produced during the takeoff in a location of the International Airport of Mexico City. As first stage, this paper presents a novel computational model that allows 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. 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.