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Outputs (6)

Comparison of Deep Learning and Psychoacoustic Models to Predict UAV Noise Impact in Soundscapes (2025)
Presentation / Conference Contribution

* The increasing prevalence of Unmanned Aircraft Systems in urban environments necessitates a deeper understanding of their impact on the experience of urban soundscapes. This study presents Machine Learning models aimed at predicting perceived annoy... Read More about Comparison of Deep Learning and Psychoacoustic Models to Predict UAV Noise Impact in Soundscapes.

Confidence limits for coherence – toward a better understanding of its use as a diffuse field metric for quantifying the quality of Direct Field Acoustic Noise (DFAN) Testing systems (2024)
Presentation / Conference Contribution

To determine the degree of diffuseness of a particular acoustic field, an oft-considered metric is the coherence between spaced pairs of measurement microphones. This is useful because the expected theoretical ‘sinc2’ trend for a diffuse field is wel... Read More about Confidence limits for coherence – toward a better understanding of its use as a diffuse field metric for quantifying the quality of Direct Field Acoustic Noise (DFAN) Testing systems.