2023
DOI: 10.1007/s10844-023-00792-2
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Audio-based anomaly detection on edge devices via self-supervision and spectral analysis

Abstract: In real-world applications, audio surveillance is often performed by large models that can detect many types of anomalies. However, typical approaches are based on centralized solutions characterized by significant issues related to privacy and data transport costs. In addition, the large size of these models prevented a shift to contexts with limited resources, such as edge devices computing. In this work we propose conv-SPAD, a method for convolutional SPectral audio-based Anomaly Detection that takes advant… Show more

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Cited by 3 publications
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