A lightweight framework for unsupervised anomalous sound detection based on selective learning of time-frequency domain features

Abstract

For industrial anomalous sound detection (ASD), self-supervised methods have achieved significant detection performance in many cases. Nevertheless, these methods typically rely on the availability of external auxiliary information, and they may not work when such information are not feasible. Unsupervised methods do not leverage auxiliary information, whereas they usually obtained lower detection performance compared to self-supervised ones. Though some unsupervised methods have shown potential performance improvements, they are at the cost of complex implementation or large model sizes. As to the issues, this paper presents an unsupervised ASD method based on spectrogram frames selection (SFS) and AutoEncoder for Frequency-feature Selection (AEFS), called SFS-AEFS. First, SFS is developed based upon the temporal characteristics of machine sounds, which aims to select spectrogram frames (SFs) that contains the primary sound information while discarding the portions that are affected by noises or interferences or do not contain the target sound. Next, AEFS is developed by introducing a Scaling Gate (SG) after AE. For the selected SF features, AEFS aims to selectively enhance the mode learning of partial frequency dimensions and weaken the rest ones. Comparative experiments with the current ASD methods were made on the DCASE 2020 Challenge Task2 dataset. The related results demonstrate that our method achieved the best performance among all relevant unsupervised methods and is comparable to the current SOTA self-supervised methods. Moreover, our method is lightweight with model parameters being only 0.08MB.