From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915172460789760 |
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| author | Barusco, Manuel Borsatti, Francesco Pezze, Davide Dalle Paissan, Francesco Farella, Elisabetta Susto, Gian Antonio |
| author_facet | Barusco, Manuel Borsatti, Francesco Pezze, Davide Dalle Paissan, Francesco Farella, Elisabetta Susto, Gian Antonio |
| contents | Recent advances in Visual Anomaly Detection (VAD) have introduced sophisticated algorithms leveraging embeddings generated by pre-trained feature extractors. Inspired by these developments, we investigate the adaptation of such algorithms to the audio domain to address the problem of Audio Anomaly Detection (AAD). Unlike most existing AAD methods, which primarily classify anomalous samples, our approach introduces fine-grained temporal-frequency localization of anomalies within the spectrogram, significantly improving explainability. This capability enables a more precise understanding of where and when anomalies occur, making the results more actionable for end users. We evaluate our approach on industrial and environmental benchmarks, demonstrating the effectiveness of VAD techniques in detecting anomalies in audio signals. Moreover, they improve explainability by enabling localized anomaly identification, making audio anomaly detection systems more interpretable and practical. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18328 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms Barusco, Manuel Borsatti, Francesco Pezze, Davide Dalle Paissan, Francesco Farella, Elisabetta Susto, Gian Antonio Sound Artificial Intelligence Computer Vision and Pattern Recognition Audio and Speech Processing Recent advances in Visual Anomaly Detection (VAD) have introduced sophisticated algorithms leveraging embeddings generated by pre-trained feature extractors. Inspired by these developments, we investigate the adaptation of such algorithms to the audio domain to address the problem of Audio Anomaly Detection (AAD). Unlike most existing AAD methods, which primarily classify anomalous samples, our approach introduces fine-grained temporal-frequency localization of anomalies within the spectrogram, significantly improving explainability. This capability enables a more precise understanding of where and when anomalies occur, making the results more actionable for end users. We evaluate our approach on industrial and environmental benchmarks, demonstrating the effectiveness of VAD techniques in detecting anomalies in audio signals. Moreover, they improve explainability by enabling localized anomaly identification, making audio anomaly detection systems more interpretable and practical. |
| title | From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms |
| topic | Sound Artificial Intelligence Computer Vision and Pattern Recognition Audio and Speech Processing |
| url | https://arxiv.org/abs/2502.18328 |