  We present the task description of the Detection and Classification of
Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: ``First-shot
unsupervised anomalous sound detection (ASD) for machine condition
monitoring''. The main goal is to enable rapid deployment of ASD systems for
new kinds of machines without the need for hyperparameter tuning. In the past
ASD tasks, developed methods tuned hyperparameters for each machine type, as
the development and evaluation datasets had the same machine types. However,
collecting normal and anomalous data as the development dataset can be
infeasible in practice. In 2023 Task 2, we focus on solving the first-shot
problem, which is the challenge of training a model on a completely novel
machine type. Specifically, (i) each machine type has only one section (a
subset of machine type) and (ii) machine types in the development and
evaluation datasets are completely different. Analysis of 86 submissions from
23 teams revealed that the keys to outperform baselines were: 1) sampling
techniques for dealing with class imbalances across different domains and
attributes, 2) generation of synthetic samples for robust detection, and 3) use
of multiple large pre-trained models to extract meaningful embeddings for the
anomaly detector.
