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<meta itemprop="description" content="Image nets detect acoustic anomalies in machinery via spectrograms.">
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<a href="http://localhost:4000/research/sound-anomaly-transfer/" itemprop="url">Sound Anomaly Transfer
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<header><h4 class="nav__title"><i class="fas fa-book"></i> Reference</h4></header>
<ol class="bibliography"><li><span id="muller2020acoustic"><span style="font-variant: small-caps">Müller, R., Ritz, F., Illium, S., and Linnhoff-Popien, C.</span> 2020. Acoustic anomaly detection for machine sounds based on image transfer learning. <i>arXiv preprint arXiv:2006.03429</i>.</span>
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<p style="display:block; width:45%" class="align-right"><img src="\assets\figures\9_image_transfer_sound_workflow.jpg" alt="Workflow diagram showing mel-spectrogram input, feature extraction via image network, and anomaly detection model" /></p>
<p>This study investigates an effective approach for <strong>acoustic anomaly detection</strong> in industrial machinery, focusing on identifying malfunctions through sound analysis. The core methodology leverages <strong>transfer learning</strong> by repurposing deep neural networks originally trained for large-scale <strong>image classification</strong> (e.g., on ImageNet) as powerful feature extractors for audio data represented as <strong>mel-spectrograms</strong>.</p>
<p>The process involves:</p>
<ol>
<li>Converting audio signals from machinery into mel-spectrogram images.</li>
<li>Feeding these spectrograms into various pretrained image classification networks (specifically comparing <strong>ResNet architectures</strong> against <strong>AlexNet</strong> and <strong>SqueezeNet</strong>) to extract deep feature representations.</li>
<li>Training standard anomaly detection models – particularly <strong>Gaussian Mixture Models (GMMs)</strong> and <strong>One-Class Support Vector Machines (OC-SVMs)</strong> – on the features extracted from normal operation sounds.</li>
<li>Classifying new sounds as anomalous if their extracted features deviate significantly from the learned normality model.</li>
</ol>
<p>Key findings from the experiments, conducted across different machine types and noise conditions, include:</p>
<ul>
<li>The proposed transfer learning approach significantly <strong>outperforms baseline methods like traditional convolutional autoencoders</strong>, especially in the presence of background noise.</li>
<li>Features extracted using <strong>ResNet architectures consistently yielded superior anomaly detection performance</strong> compared to those from AlexNet and SqueezeNet.</li>
<li><strong>GMMs and OC-SVMs proved highly effective</strong> as anomaly detection classifiers when applied to these transferred features.</li>
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<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>
<p>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. <a class="citation" href="/publications#muller2020acoustic">[Müller et al. 2020]</a></p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2021-03-02T00:00:00+01:00">March 2, 2021</time></p>
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