Paper Info
Title
Optimizing acoustic feature extractor for anomalous sound detection based on Neyman-Pearson lemma.
Abstract
We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous sounds. However, the ideal property of a set of acoustic features that maximizes ASD performance has not been clarified. By considering outlier-detection-based ASD as a statistical hypothesis test, we defined optimality as an objective function that adopts Neyman-Pearson lemma; the acoustic feature extractor is optimized to extract a set of acoustic features which maximize the true positive rate under an arbitrary false positive rate. The variational auto-encoder is applied as an acoustic feature extractor and optimized to maximize the objective function. We confirmed that the proposed method improved the F-measure score from 0.02 to 0.06 points compared to those of conventional methods, and ASD results of a stereolithography 3D-printer in a real-environment show that the proposed method is effective in identifying anomalous sounds.
Year
Venue
Keywords
2017
European Signal Processing Conference
Anomalous sound detection,acoustic feature,objective function,deep neural network,Gaussian mixture model
Field
DocType
ISSN
False positive rate,Signal processing,Pattern recognition,Sound detection,Computer science,Feature extraction,Artificial intelligence,Linear programming,Neyman–Pearson lemma,Lemma (mathematics),Statistical hypothesis testing
Conference
2076-1465
Citations 
PageRank 
References 
1
0.36
13
Authors
4
Name
Order
Citations
PageRank
Koizumi Yuma14111.75
Shoichiro Saito2132.88
Hisashi Uematsu331.10
Harada Noboru46725.07