2024/03/21 by Kevin Wilkinghoff, Wilkinghoff, Kevin · 2 citations
Computer Science · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Music and Audio Processing #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.14179
openalex publication_date 2024/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 for training the embedding model 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 while still ensuring an angular margin between classes. By doing so, the resulting distributions of the 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 anomalous sound detection datasets, it is shown that using AdaProj to learn an embedding space significantly outperforms other commonly used loss functions.