Grollmisch, SaschaSaschaGrollmischJohnson, DavidDavidJohnsonAbeßer, JakobJakobAbeßerLukashevich, HannaHannaLukashevich2022-03-142024-02-292022-03-142020https://publica.fraunhofer.de/handle/publica/409921In this technical report, we present our system for task 2 of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE2020 Challenge): Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The focus of this task is to detect anomalous industrial machine sounds using an acoustic quality control system, which is only trained with sound samples from the normal (machine) condition. The dataset covers a variety of machines ranging from stable sound sources such as car engines, to transient sounds such as opening and closing valves. Our proposed method combines pre-trained OpenL3 embeddings with the reconstruction error of an interpolation autoencoder using a gaussian mixture model as the final predictor. The optimized model achieved 88.5% AUC and 76.8% pAUC on average over all machines and types provided with the development dataset, and outperformed the published baseline by 14.9% AUC and 17.2% pAUC.enAnalyse Industriegeräusche621006presentation