  Use of an autoencoder (AE) as a normal model is a state-of-the-art technique
for unsupervised-anomaly detection in sounds (ADS). The AE is trained to
minimize the sample mean of the anomaly score of normal sounds in a mini-batch.
One problem with this approach is that the anomaly score of rare-normal sounds
becomes higher than that of frequent-normal sounds, because the sample mean is
strongly affected by frequent-normal samples, resulting in preferentially
decreasing the anomaly score of frequent-normal samples. To decrease anomaly
scores for both frequent- and rare-normal sounds, we propose batch
uniformization, a training method for unsupervised-ADS for minimizing a
weighted average of the anomaly score on each sample in a mini-batch. We used
the reciprocal of the probabilistic density of each sample as the weight, more
intuitively, a large weight is given for rare-normal sounds. Such a weight
works to give a constant anomaly score for both frequent- and rare-normal
sounds. Since the probabilistic density is unknown, we estimate it by using the
kernel density estimation on each training mini-batch. Verification- and
objective-experiments show that the proposed batch uniformization improves the
performance of unsupervised-ADS.
