Under CopyrightBardeli, RolfRolfBardeliStein, DanielDanielStein2022-03-1222.3.20132012https://publica.fraunhofer.de/handle/publica/37754910.24406/publica-fhg-3775492-s2.0-850913342562-s2.0-84947967209Although abnormal events in an audio stream are by their nature hard to define, a continuous monitoring of audio surveillance data can detect crucial information in, e.g., train engines that might require critical maintenance. Our method detects abnormal events without being trained on a certain situation, by building a model of the expected sound environment given a continuously adapting history of past audio material within a limited time interval. We evaluate the precision of this method on recordings from train rides.enabnormal event detectionaudiosound environmentadapting history005006629conference paper