This paper proposes an approach for anomalous sound detection that incorporates outlier exposure and inlier modeling within a unified framework by multitask learning. While outlier exposure-based methods can extract features efficiently, it is not robust. Inlier modeling is good at generating robust features, but the features are not very effective. Recently, serial approaches are proposed to combine these two methods, but it still requires a separate training step for normal data modeling. To overcome these limitations, we use multitask learning to train a conformer-based encoder for outlier-aware inlier modeling. Moreover, our approach provides multi-scale scores for detecting anomalies. Experimental results on the MIMII and DCASE 2020 task 2 datasets show that our approach outperforms state-of-the-art single-model systems and achieves comparable results with top-ranked multi-system ensembles.

翻译：本文提出一种基于多任务学习的统一框架，将离群暴露与内点建模相结合以实现异常声音检测。基于离群暴露的方法虽能高效提取特征，但鲁棒性不足；内点建模擅长生成鲁棒特征，但其特征有效性较低。近期研究提出串行方法结合二者，但仍需单独训练正常数据建模步骤。为克服这些局限，我们采用多任务学习训练基于Conformer的编码器，实现离群感知的内点建模。此外，本方法提供多尺度评分以检测异常。在MIMII与DCASE 2020任务2数据集上的实验表明，本方法性能优于当前最先进的单模型系统，且与顶级多系统集成结果相当。