An Ensemble Approach to Unsupervised Anomalous Sound Detection

The task of anomalous sound detection (ASD) is to determine whether an observed sound is anomalous or normal. Both supervised and unsupervised approach can be adopted for the ASD task. In supervised approach anomalous and normal data are used in training whereas in unsupervised approach only normal data are used for training. In this work, we provide an overview of the systems developed for the task 2 i.e., unsupervised detection of anomalous sounds for machine condition monitoring, of the DCASE 2020 challenge. We employ various handcrafted local representations from the short-time spectral analysis of sounds. We also use fisher vector encoding a learned global representations obtained from local representations of sound. Autoencoder variants and copy detection approaches are applied on the top of local representations and a standard GMM classifier is used with fisher vector encodings for unsupervised detection of anomalous sounds.