  In this work, we thoroughly evaluate the efficacy of pretrained neural
networks as feature extractors for anomalous sound detection. In doing so, we
leverage the knowledge that is contained in these neural networks to extract
semantically rich features (representations) that serve as input to a Gaussian
Mixture Model which is used as a density estimator to model normality. We
compare feature extractors that were trained on data from various domains,
namely: images, environmental sounds and music. Our approach is evaluated on
recordings from factory machinery such as valves, pumps, sliders and fans. All
of the evaluated representations outperform the autoencoder baseline with music
based representations yielding the best performance in most cases. These
results challenge the common assumption that closely matching the domain of the
feature extractor and the downstream task results in better downstream task
performance.
