29 lines
2.8 KiB
Plaintext
29 lines
2.8 KiB
Plaintext
---
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title: "Sound Anomaly Transfer"
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tags: [anomaly-detection, audio-classification, deep-learning, transfer-learning, feature-extraction, computer-vision, industrial-monitoring, machine-learning]
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excerpt: "Image nets detect acoustic anomalies in machinery via spectrograms."
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teaser: "/figures/9_image_transfer_sound_teaser.jpg"
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venue: "ICAART 2021"
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---
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<FloatingImage src="/figures/9_image_transfer_sound_workflow.jpg" alt="Workflow of sound anomaly detection using image transfer learning" width={800} height={400} float="right" caption="Overall workflow for acoustic anomaly detection using transfer learning from image classification models." />
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This study investigates an effective approach for **acoustic anomaly detection** in industrial machinery, focusing on identifying malfunctions through sound analysis.
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The core methodology leverages **transfer learning** by repurposing deep neural networks originally trained for large-scale **image classification** (e.g., on ImageNet) as powerful feature extractors for audio data represented as **mel-spectrograms**.
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The process involves:
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1. Converting audio signals from machinery into mel-spectrogram images.
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2. Feeding these spectrograms into various pretrained image classification networks (specifically comparing **ResNet architectures** against **AlexNet** and **SqueezeNet**) to extract deep feature representations.
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3. Training standard anomaly detection models – particularly **Gaussian Mixture Models (GMMs)** and **One-Class Support Vector Machines (OC-SVMs)** – on the features extracted from normal operation sounds.
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4. Classifying new sounds as anomalous if their extracted features deviate significantly from the learned normality model.
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Key findings from the experiments, conducted across different machine types and noise conditions, include:
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* The proposed transfer learning approach significantly **outperforms baseline methods like traditional convolutional autoencoders**, especially in the presence of background noise.
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* Features extracted using **ResNet architectures consistently yielded superior anomaly detection performance** compared to those from AlexNet and SqueezeNet.
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* **GMMs and OC-SVMs proved highly effective** as anomaly detection classifiers when applied to these transferred features.
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<CenteredImage src="/figures/9_image_transfer_sound_mels.jpg" alt="Examples of mel-spectrograms from normal and anomalous machine sounds" width={1024} height={400} caption="Mel-spectrogram examples illustrating the difference between normal and anomalous machine sounds." maxWidth="75%" />
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This work demonstrates the surprising effectiveness of transferring knowledge from the visual domain to the acoustic domain for anomaly detection, offering a robust and readily implementable method for monitoring industrial equipment. <Cite bibtexKey="muller2020acoustic" /> |