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_posts/research/2021-03-02-sound-anomaly-transfer.md

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+---
+layout: single
+title: "Sound Anomaly Transfer"
+categories: research
+tags: anomaly-detection audio-classification deep-learning transfer-learning feature-extraction computer-vision
+excerpt: "Image nets detect acoustic anomalies in machinery via spectrograms."
+header:
+  teaser: /assets/figures/9_image_transfer_sound_teaser.jpg
+scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
+---
+
+![Workflow diagram showing mel-spectrogram input, feature extraction via image network, and anomaly detection model](\assets\figures\9_image_transfer_sound_workflow.jpg)
+{:style="display:block; width:45%" .align-right}
+
+
+This study investigates an effective approach for **acoustic anomaly detection** in industrial machinery, focusing on identifying malfunctions through sound analysis. 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**.
+
+The process involves:
+1.  Converting audio signals from machinery into mel-spectrogram images.
+2.  Feeding these spectrograms into various pretrained image classification networks (specifically comparing **ResNet architectures** against **AlexNet** and **SqueezeNet**) to extract deep feature representations.
+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.
+4.  Classifying new sounds as anomalous if their extracted features deviate significantly from the learned normality model.
+
+Key findings from the experiments, conducted across different machine types and noise conditions, include:
+
+*   The proposed transfer learning approach significantly **outperforms baseline methods like traditional convolutional autoencoders**, especially in the presence of background noise.
+*   Features extracted using **ResNet architectures consistently yielded superior anomaly detection performance** compared to those from AlexNet and SqueezeNet.
+*   **GMMs and OC-SVMs proved highly effective** as anomaly detection classifiers when applied to these transferred features.
+
+<div style="clear: both;"></div>
+
+<center>
+  <img src="/assets/figures/9_image_transfer_sound_mels.jpg" alt="Examples of mel-spectrograms from normal and anomalous machine sounds" style="display:block; width:85%">
+  <figcaption>Mel-spectrogram examples of normal vs. anomalous machine sounds.</figcaption>
+</center>
+
+
+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 muller2020acoustic %}