Data-driven Non-uniform Filterbanks Based on F-ratio for Machine Anomalous Sound Detection

Author:

Li Kai1,Tran Dung Kim1,Lu Xugang2,Akagi Masato1,Unoki Masashi1

Affiliation:

1. School of Information Science, Japan Advanced Institute of Science and Technology,Japan

2. National Institute of Information and Communications Technology,Advanced Speech Technology Laboratory,Japan

Publisher

IEEE

Reference24 articles.

1. Self-supervised classification for detecting anomalous sounds;giri;Proc 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE),0

2. Adam: A method for stochastic optimization;kingma;ArXiv Preprint,2014

3. Anomalous sound detection using cnn-based features by self supervised learning;morita;DCASE2021 Challenge Tech Rep,2021

4. ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions;harada;Proc 6th DCASE,2021

5. Flow-Based Self-Supervised Density Estimation for Anomalous Sound Detection

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