Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection
Published in arXiv preprint arXiv:2410.03904, 2024
Audio anomaly detection is crucial for applications ranging from industrial monitoring to smart home security. However, progress in this field is limited by the lack of comprehensive benchmark datasets. This paper introduces AADG (Audio Anomaly Data Generator), a framework for generating synthetic benchmark data for audio anomaly detection. Our framework allows researchers to create customized datasets with controllable anomaly types, signal-to-noise ratios, and background conditions.
Recommended citation: @article{raghavan2024did, title={Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection}, author={Raghavan, Ksheeraja and Gode, Samiran and Shah, Ankit and Raghavan, Surabhi and Burgard, Wolfram and Raj, Bhiksha and Singh, Rita}, journal={arXiv preprint arXiv:2410.03904}, year={2024} }
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