  We propose discriminative neighborhood smoothing of generative anomaly scores
for anomalous sound detection. While the discriminative approach is known to
achieve better performance than generative approaches often, we have found that
it sometimes causes significant performance degradation due to the discrepancy
between the training and test data, making it less robust than the generative
approach. Our proposed method aims to compensate for the disadvantages of
generative and discriminative approaches by combining them. Generative anomaly
scores are smoothed using multiple samples with similar discriminative features
to improve the performance of the generative approach in an ensemble manner
while keeping its robustness. Experimental results show that our proposed
method greatly improves the original generative method, including absolute
improvement of 22% in AUC and robustly works, while a discriminative method
suffers from the discrepancy.
