Robust Audio Anomaly Detection
- URL: http://arxiv.org/abs/2202.01784v1
- Date: Thu, 3 Feb 2022 17:19:42 GMT
- Title: Robust Audio Anomaly Detection
- Authors: Wo Jae Lee, Karim Helwani, Arvindh Krishnaswamy, Srikanth Tenneti
- Abstract summary: The presented approach doesn't assume the presence of labeled anomalies in the training dataset.
The temporal dynamics are modeled using recurrent layers augmented with attention mechanism.
The output of the network is an outlier robust probability density function.
- Score: 10.75127981612396
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose an outlier robust multivariate time series model which can be used
for detecting previously unseen anomalous sounds based on noisy training data.
The presented approach doesn't assume the presence of labeled anomalies in the
training dataset and uses a novel deep neural network architecture to learn the
temporal dynamics of the multivariate time series at multiple resolutions while
being robust to contaminations in the training dataset. The temporal dynamics
are modeled using recurrent layers augmented with attention mechanism. These
recurrent layers are built on top of convolutional layers allowing the network
to extract features at multiple resolutions. The output of the network is an
outlier robust probability density function modeling the conditional
probability of future samples given the time series history. State-of-the-art
approaches using other multiresolution architectures are contrasted with our
proposed approach. We validate our solution using publicly available machine
sound datasets. We demonstrate the effectiveness of our approach in anomaly
detection by comparing against several state-of-the-art models.
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