Understanding acoustic characteristics of aircraft is critical for designing optimal fleet compositions in terms of noise and improved airport operations. This study investigates acoustic signatures across different aircraft types, engine designs, and operational conditions. A dataset consisting of 457 field acoustic measurements of commercial turbofan aircraft landing and taking-off from Amsterdam Airport Schiphol was used. To unveil meaningful patterns, we focused on dimensionality reduction techniques—Principal Component Analysis (PCA) and tdistributed Stochastic Neighbour Embedding (t-SNE)— to analyse this high-dimensional acoustic data. These methods are complemented by clustering algorithms and supervised machine learning models, such as K-Means, random forests for feature importance, and multilayer perceptrons (MLP) to classify aircraft types, engine configurations, and operations. Results reveal a strong loudness axis in the first principal component, overshadowing subtle spectral and timebased differences across aircraft families, especially for takeoffs. Nonetheless, focusing on higher-order components and alternative embeddings (t-SNE) highlights additional spectral and temporal markers. Operation classification (landing vs. takeoff) achieves 98% accuracy, but aircraft and engine family classification remain challenging, with accuracy capped below 50% using these feature sets. These findings suggest that advanced feature selection and dimensionality reduction while considering amplitude characteristics are essential for disentangling nuanced design-based acoustic traits.

This manuscript presents a psychoacoustic analysis of the noise emissions from the Airbus A320 aircraft family, with a special focus on the tonal noise emitted by its nose landing gear (NLG) system. The study is based on microphone array measurements of aircraft flyovers under operational conditions performed next to Amsterdam Airport Schiphol. It was found that the NLG system is a dominant tonal noise source for all aircraft subtypes measured (A319, A320, A320neo, A321, and A321neo) around 1700 Hz. The magnitude of the tonal noise observed was strongly correlated with the aircraft velocity, whereas the tonal frequency remained relatively constant. A preliminary psychoacoustic analysis between the different aircraft subtypes showed that, on average, the neo versions presented higher metric values for effective perceived noise level (EPNL), psychoacoustic annoyance, and loudness but lower tonality than their older ceo counterparts. However, given the low number of neo samples available in this study, no strong conclusion can be drawn from this analysis. Overall, the A321 aircraft measured presented lower average values for all noise metrics evaluated than the A320 ones, despite being larger and heavier. These claims will be evaluated in upcoming dedicated psychoacoustic listening experiments.

The impressive growth of the aviation industry and the number of flights entail several environmental repercussions, such as increased aircraft noise emissions. With the worrying number of complaints from the communities around airports comes also the distrust in numerical models used for aircraft noise prediction. In this study, we compare the ‘Dutch aircraft noise model’ predictions to measured values from the NOise MOnitoring System (NOMOS) around Amsterdam Airport Schiphol between 2012 and 2018. While the model underestimates aircraft noise in 2012, the model prediction improved throughout the years. We observe a decreasing trend of measured aircraft-related L_den values of 0.6dB(A)/year (a total of 3.6dB(A) over the investigation period), although the total number of flight movements increased during the observation time. We propose that a change in fleet mix, as well as the implementation of Noise Abatement Procedures at Schiphol Airport, fuelled this trend.