The state-of-the-art approach for semi-supervised anomalous sound detection is to first learn an embedding space by using auxiliary classification tasks based on meta information or self-supervised learning and then estimate the distribution of normal data. In this work, AdaProj a novel loss function is presented. In contrast to commonly used angular margin losses, which project data of each class as close as possible to their corresponding class centers, AdaProj learns to project data onto class-specific subspaces. By doing so, the resulting distributions of embeddings belonging to normal data are not required to be as restrictive as other loss functions allowing a more detailed view on the data. In experiments conducted on the DCASE2022 and DCASE2023 datasets, it is shown that using AdaProj to learn an embedding space significantly outperforms other commonly used loss functions and results in a state-of-the-art performance on the DCASE2023 dataset.

翻译：半监督异常声音检测的前沿方法首先基于元信息或自监督学习的辅助分类任务学习嵌入空间，然后估计正常数据的分布。本文提出一种新型损失函数AdaProj。与通常将所有类别的数据投影到其对应类别中心附近的角间隔损失不同，AdaProj学习将数据投影到类别特定子空间。通过这种方式，属于正常数据的嵌入向量分布无需像其他损失函数那样具有严格约束，从而能够更细致地观察数据。在DCASE2022和DCASE2023数据集上的实验表明，使用AdaProj学习嵌入空间显著优于其他常用损失函数，并在DCASE2023数据集上达到了当前最优性能。