3 accepted papers
In this paper, we propose an effective anomalous sound detection (ASD) method based on representation learning with simulated anomalies. Recently, ASD systems have used Outlier Exposure (OE) strategy to achieve promising performance in DCASE challenges. These exploit deep Convolutional Neural Networ…
In this paper, we propose a joint generative and contrastive representation learning method (GeCo) for anomalous sound detection (ASD). GeCo exploits a Predictive AutoEncoder (PAE) equipped with self-attention as a generative model to perform frame-level prediction. The output of the PAE together wi…
Automatic speaker verification (ASV) faces domain shift caused by the mismatch of intrinsic and extrinsic factors, such as recording device and speaking style, in real-world applications, which leads to severe performance degradation. Since single-speaker multi-condition (SSMC) data is difficult to…