MODULATION SPECTRAL SIGNAL REPRESENTATION AND I-VECTORS FOR ANOMALOUS SOUND DETECTION Technical Report

This report summarizes our submission for Task-2 of the DCASE 2020 Challenge. We propose two different anomalous sound detection systems, one based on features extracted from a modulation spectral signal representation and the other based on i-vectors extracted from mel-band features. The first system uses a nearest neighbour graph to construct clusters which capture local variations in the training data. Anomalies are then identified based on their distance from the cluster centroids. The second system uses i-vectors extracted from mel-band spectra for training a Gaussian Mixture Model. Anomalies are then identified using their negative log likelihood. Both these methods show significant improvement over the DCASE Challenge baseline AUC scores, with an average improvement of 6% across all machines. An ensemble of the two systems is shown to further improve the average performance by 11% over the baseline.