Improvements of Discriminative Feature Space Training for Anomalous Sound Detection in Unlabeled Conditions
<|reference_start|>Improvements of Discriminative Feature Space Training for Anomalous Sound Detection in Unlabeled Conditions: In anomalous sound detection, the discriminative method has demonstrated superior performance. This approach constructs a discriminative feature space through the classification of the meta-information labels for normal sounds. This feature space reflects the differences in machine sounds and effectively captures anomalous sounds. However, its performance significantly degrades when the meta-information labels are missing. In this paper, we improve the performance of a discriminative method under unlabeled conditions by two approaches. First, we enhance the feature extractor to perform better under unlabeled conditions. Our enhanced feature extractor utilizes multi-resolution spectrograms with a new training strategy. Second, we propose various pseudo-labeling methods to effectively train the feature extractor. The experimental evaluations show that the proposed feature extractor and pseudo-labeling methods significantly improve performance under unlabeled conditions.<|reference_end|>
@article{fujimura2024improvements,
    title={Improvements of Discriminative Feature Space Training for Anomalous
  Sound Detection in Unlabeled Conditions},
    author={Takuya Fujimura, Ibuki Kuroyanagi, and Tomoki Toda},
    journal={arXiv preprint arXiv:2409.09332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09332},
    primaryClass={eess.AS cs.SD}
}