Detection of Anomalous Sounds for Machine Condition Monitoring using Classification Confidence

Anomaly-detection methods based on classification confidence are applied to the DCASE 2020 Task 2 Challenge on Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The final systems for submitting to the challenge are ensembles of two classification-based detectors. Both classifiers are trained with either known or generated properties of normal sounds as labels: one is a model to classify sounds into machine type and ID; the other is a model to classify transformed sounds into dataaugmentation type. As for the latter model, the normal sound is augmented by using sound-transformation techniques such as pitch shifting, and data-augmentation type is used as a label. For both classifiers, classification confidence is used as the normality score for an input sample at runtime. An ensemble of these approaches is created by using probability aggregation of their anomaly scores. The experimental results on AUC show superior performance by each detector in relation to the baseline provided by the DCASE organizer. Moreover, the proposed ensemble of two detectors generally shows further improvement on the anomaly detection performance. The proposed anomaly-detection system was ranked fourth in the team ranking according to the metrics of the DCASE Challenge, and it achieves 90.93% in terms of average of AUC and pAUC scores for all the machine types, and that score is the highest of those scores achieved by all of the submitted systems.

[1]  Jing Gao,et al.  Converting Output Scores from Outlier Detection Algorithms into Probability Estimates , 2006, Sixth International Conference on Data Mining (ICDM'06).

[2]  Hans-Peter Kriegel,et al.  Interpreting and Unifying Outlier Scores , 2011, SDM.

[3]  Thomas Brox,et al.  Discriminative Unsupervised Feature Learning with Convolutional Neural Networks , 2014, NIPS.

[4]  Yu Qiao,et al.  A Discriminative Feature Learning Approach for Deep Face Recognition , 2016, ECCV.

[5]  Randy C. Paffenroth,et al.  Anomaly Detection with Robust Deep Autoencoders , 2017, KDD.

[6]  Giovanni De Magistris,et al.  Spatio-Temporal Anomaly Detection for Industrial Robots through Prediction in Unsupervised Feature Space , 2017, 2017 IEEE Winter Conference on Applications of Computer Vision (WACV).

[7]  Kevin Gimpel,et al.  A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks , 2016, ICLR.

[8]  Georg Langs,et al.  Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery , 2017, IPMI.

[9]  Ran El-Yaniv,et al.  Deep Anomaly Detection Using Geometric Transformations , 2018, NeurIPS.

[10]  Alexander Binder,et al.  Deep One-Class Classification , 2018, ICML.

[11]  Shiqiang Wang,et al.  DOMESTIC ACTIVITIES CLASSIFICATION BASED ON CNN USING SHUFFLING AND MIXING DATA AUGMENTATION Technical Report , 2018 .

[12]  Yohei Kawaguchi,et al.  MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection , 2019, DCASE.

[13]  Vishal M. Patel,et al.  Learning Deep Features for One-Class Classification , 2018, IEEE Transactions on Image Processing.

[14]  Yuma Koizumi,et al.  ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection , 2019, 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[15]  Raghavendra Chalapathy University of Sydney,et al.  Deep Learning for Anomaly Detection: A Survey , 2019, ArXiv.

[16]  Dawn Song,et al.  Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty , 2019, NeurIPS.

[17]  Asim Munawar,et al.  Adversarial Discriminative Attention for Robust Anomaly Detection , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[18]  Takashi Endo,et al.  Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring , 2020, DCASE.

[19]  Alexander Binder,et al.  Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).