Dynamic hierarchical aggregation of parallel outputs for aircraft take-off noise identification

Assessment of airport noise pollution mainly depends on the correlation between aircraft class, noise measured and flight path geometry. Regulation, evaluation and especially certification procedures generally establish that previous correlation cannot be carried out using aircraft navigation systems data. Additionally, airport noise monitoring systems generally use aircraft noise signals only for computing statistical indicators. Consequently, methods to acquire more information from these signals have been explored so as to improve noise estimation around airports. In this regard, this paper introduces a new model for aircraft class recognition based on take-off noise signal segmentation and dynamic hierarchical aggregation of K   parallel neural networks outputs Opk. A single hierarchy is separately defined for every class p, mainly based on the recall and precision of neural network NNk|k=1,2,…,K. Similarly, the dynamics proposed is also particular to each class p. The performance of the new model is benchmarked against models in literature over a database containing real-world take-off noise measurements. The new model performs better on the abovementioned database and successfully classifies over 89% of measurements.