Aircraft noise is an increasingly important issue that causes annoyance and complaints for the communities living in the vicinity of airports. The conventional sound metrics (such as the A—weighted sound pressure level) typically used for assessing the impact of aircraft noise often fail to conveniently represent the actual annoyance experienced. More sophisticated sound quality metrics (such as loudness, tonality and sharpness) can be used to determine the psychoacoustic annoyance perceived by the human ear. In this paper, an Airbus A320 landing flyover under operational conditions recorded with a microphone array is analyzed. The application of functional beamforming to the acoustic data allows for the separation of the emissions of different noise sources on board of the aircraft. For this case, the nose landing gear (NLG) and the turbofan engines were selected, due to their expected dominance during approach. It was found that, despite being more quiet than the turbofan engines, the NLG system emits a strong tonal sound that makes it overall more annoying than the noise of the engines. Airframe noise prediction models (Fink's and Guo's methods) do not consider this tone and considerably underpredict the noise levels and the annoyance of the NLG. Thus, it is highly recommended to employ these sound quality metrics to study aircraft noise and to evaluate the potential improvement in annoyance of future low—noise aircraft concepts, rather than just a difference in the sound pressure level in decibels.
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