Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds

• URL: http://arxiv.org/abs/2006.10417v2
• Date: Fri, 19 Jun 2020 09:06:35 GMT
• Title: Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds
• Authors: Alexandrine Ribeiro, Luis Miguel Matos, Pedro Jose Pereira, Eduardo C. Nunes, Andre L. Ferreira, Paulo Cortez, Andre Pilastri
• Abstract summary: This report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process. The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features.
• Score: 55.18259748448095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This technical report describes two methods that were developed for Task 2 of the DCASE 2020 challenge. The challenge involves an unsupervised learning to detect anomalous sounds, thus only normal machine working condition samples are available during the training process. The two methods involve deep autoencoders, based on dense and convolutional architectures that use melspectogram processed sound features. Experiments were held, using the six machine type datasets of the challenge. Overall, competitive results were achieved by the proposed dense and convolutional AE, outperforming the baseline challenge method.

Related papers

• Robust Tiny Object Detection in Aerial Images amidst Label Noise [50.257696872021164]
  This study addresses the issue of tiny object detection under noisy label supervision. We propose a DeNoising Tiny Object Detector (DN-TOD), which incorporates a Class-aware Label Correction scheme. Our method can be seamlessly integrated into both one-stage and two-stage object detection pipelines.
  arXiv  Detail & Related papers  (2024-01-16T02:14:33Z)
• AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling [0.0]
  Anomaly detection or more generally outliers detection is one of the most popular and challenging subject in theoretical and applied machine learning. We present a new semi-supervised anomaly detection method called textbfAnoRand by combining a deep learning architecture with random synthetic label generation.
  arXiv  Detail & Related papers  (2023-05-28T10:53:34Z)
• Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining [52.191658157204856]
  This paper uses contrastive learning to refine audio representations for each machine ID, rather than for each audio sample. The proposed two-stage method uses contrastive learning to pretrain the audio representation model. Experiments show that our method outperforms the state-of-the-art methods using contrastive learning or self-supervised classification.
  arXiv  Detail & Related papers  (2023-04-07T11:08:31Z)
• Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approach [17.85309428707623]
  AEGAN-AD is a totally unsupervised approach in which the generator is trained to reconstruct input spectrograms. The performance of AEGAN-AD on the dataset of DCASE 2022 Challenge TASK 2 demonstrates the state-of-the-art result.
  arXiv  Detail & Related papers  (2023-03-31T10:27:36Z)
• Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning [3.0100975935933567]
  We present novel weakly-supervised approaches to anomaly detection for industrial settings. The approaches make use of a Digital Twin to generate a training dataset which simulates the normal operation of the machinery. The performance of the proposed methods is compared against various state-of-the-art anomaly detection algorithms on an application to a real-world dataset.
  arXiv  Detail & Related papers  (2020-11-12T10:15:56Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection [0.0]
  We introduce ID-Conditioned Auto-Encoder for unsupervised anomaly detection. Our method is an adaptation of the Class-Conditioned Auto-Encoder (C2AE) designed for the open-set recognition. We evaluate our method on the ToyADMOS and MIMII datasets from the DCASE 2020 Challenge Task 2.
  arXiv  Detail & Related papers  (2020-07-10T11:24:20Z)
• Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring [36.60410256763345]
  We present the results of the DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to identify whether the sound emitted from a target machine is normal or anomalous.
  arXiv  Detail & Related papers  (2020-06-10T13:17:36Z)
• Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching [69.24460241328521]
  Machine learning algorithms can be negatively affected by mismatches between training (source) and test (target) data distributions. We propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset. We show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.
  arXiv  Detail & Related papers  (2020-04-30T23:56:05Z)
• Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification [93.2334223970488]
  We propose two regularizers to prevent hypersphere collapse in deep SVDD. The first regularizer is based on injecting random noise via the standard cross-entropy loss. The second regularizer penalizes the minibatch variance when it becomes too small.
  arXiv  Detail & Related papers  (2020-01-24T03:44:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.