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This paper introduces a new dataset called "ToyADMOS" designed for anomaly detection in machine operating sounds (ADMOS).
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Y. Koizumi, S. Murata, N. Harada, S. Saito, and H. Uematsu, “SNIPER: Few-shot Learning for Anomaly Detection to Minimize False-Negative Rate with Ensured True-Positive Rate,”
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A. Mesaros, T. Heittola, A. Diment, B. Elizalde, A. Shah, E. Vincent, B. Raj, and T. Virtanen, “DCASE 2017 Challenge Setup: Tasks, Datasets and Baseline system,”
2017
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H. Lim, J. Park and Y. Han, “Rare Sound Event Detection Using 1D Convolutional Recurrent Neural Networks,”
2017
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E. Cakir and T. Virtanen, “Convolutional Recurrent Neural Networks for Rare Sound Event Detection,”
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Y. Kawachi, Y. Koizumi, and N. Harada, “Complementary Set Variational Autoencoder for Supervised Anomaly Detection,”
2018
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2019
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Y. Kawachi, Y. Koizumi, S. Murata, and N. Harada, “A Two-Class Hyper-Spherical Autoencoder for Supervised Anomaly Detection,”
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M. Yamaguchi, Y. Koizumi, and N. Harada, “AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Transition,”
2019
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Y. Kawaguchi, R. Tanabe, T. Endo, K. Ichige and K. Hamada, “Anomaly Detection based on an Ensemble of Dereverberation and Anomalous Sound Extraction,”
2019
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