# Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds

## TL;DR

This paper introduces batch uniformization, a novel training method for unsupervised anomaly detection in sounds that balances anomaly scores across frequent and rare normal sounds, improving detection performance.

## Contribution

The paper proposes batch uniformization, a new training approach that uses kernel density estimation to equalize anomaly scores for normal sounds in DNN-based anomaly detection.

## Key findings

- Improved anomaly detection performance in experiments.
- Effective balancing of anomaly scores for rare and frequent normal sounds.
- Enhanced robustness of unsupervised anomaly detection methods.

## Abstract

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.