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The invasive Asian hornet (Vespa velutina) poses a threat to insect biodiversity, pollination services, and honey production. Current control strategies rely on locating and destroying nests. One method to locate nests is by using a wick bait station and recording return times of hornets, which allows an estimation of nest distance to the station. This approach could potentially be automated using acoustic sensing at bait stations. To achieve this, it is necessary to distinguish individuals based on their acoustic signatures.As a first step, this paper presents a characterisation of hornet flight sounds under low-noise conditions. Flight sound recordings were collected from 68 Asian hornets and 9 European hornets (Vespa crabro). Each hornet was recorded individually inside a mesh enclosure within an anechoic chamber using a calibrated microphone. The resulting dataset contains 2.5 hours of isolated flight sound from Asian hornets and half an hour from European hornets.The analysis focuses on the fundamental wingbeat frequency (f₀) and its variability within and between individuals. Among hornets exhibiting >20 s of flight events (51 Asian, 9 European), the mean individual f₀ was 110 Hz for Asian hornets and 115 Hz for European hornets, with average within-individual standard deviations of 3.8 Hz and 4.5 Hz, respectively. Initial results indicate noticeable inter-individual variability in fundamental frequency, suggesting that hornet flight sounds may carry information useful for discriminating individuals.Although this study is limited to low-noise measurements and restricted acoustic features, it establishes a reference dataset and methodology for future work. The findings support future work involving additional features, machine learning, and outdoor validation.