Many airports all over the world have established restrictions for the use of thrust reverse for slowing down aircraft after landings, especially during the night period, as a way of reducing noise impact and the number of complaints in the vicinity of airports. This is the case of Madrid airport, where the Universidad Politécnica de Madrid, in collaboration with AENA, and the Politecnico di Milano have been researching, and developing intelligent instruments to improve the detection and classification of thrust reverse noise among other noise sources present in the airport. Based on a traditional approach, the thrust reverse noise detection tool detects two consecutive sound events, and applies pattern recognition techniques for the classification of each of them (such as landing and thrust reverse). A second improvement refers to the use of a microphone array linked to a noise- monitoring unit, which enables tracking the direction of arrival of the sound, thus improving the classification rates. By taking the latter, it is also possible to track the aircraft location along the runway, which enables sound pressure measurements to be transformed into sound power level estimations. Although the novel instrument can still be optimized and customized, the results have shown quite good classification rates (over 90%).
Modeling and Mapping of Combined Noise Annoyance for Aircraft and Road Traffic Based on a Partial Loudness Model