Acoustic scene classification (ASC) is a crucial research problem in computational auditory scene analysis, and it aims to recognize the unique acoustic characteristics of an environment. One of the challenges of the ASC task is domain shift caused by a distribution gap between training and testing data. Since 2018, ASC challenges have focused on the generalization of ASC models across different recording devices. Although this task in recent years has achieved substantial progress in device generalization, the challenge of domain shift between different regions, involving characteristics such as time, space, culture, and language, remains insufficiently explored at present. In addition, considering the abundance of unlabeled acoustic scene data in the real world, it is important to study the possible ways to utilize these unlabelled data. Therefore, we introduce the task Semi-supervised Acoustic Scene Classification under Domain Shift in the ICME 2024 Grand Challenge. We encourage participants to innovate with semi-supervised learning techniques, aiming to develop more robust ASC models under domain shift.

翻译：声学场景分类（ASC）是计算听觉场景分析中的关键研究问题，旨在识别环境的独特声学特征。ASC任务的挑战之一是由训练数据与测试数据分布差异导致的域迁移问题。自2018年以来，ASC挑战赛重点关注不同录音设备下ASC模型的泛化能力。尽管近年来该任务在设备泛化方面取得显著进展，但涉及时间、空间、文化及语言等特性的跨区域域迁移挑战目前仍未得到充分探索。此外，考虑到现实世界中存在大量未标注的声学场景数据，如何利用这些未标注数据具有重要意义。为此，我们在ICME 2024大挑战赛中提出"域迁移下的半监督声学场景分类"任务，鼓励参赛者创新半监督学习技术，旨在开发在域迁移条件下更具鲁棒性的ASC模型。