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 the domain shift 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 geographical regions, involving discrepancies 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：声学场景分类是计算听觉场景分析中的一个关键研究问题，旨在识别环境的独特声学特征。该任务面临的挑战之一是训练数据与测试数据之间的领域转移。自2018年以来，声学场景分类挑战赛聚焦于不同录音设备下声学场景分类模型的泛化能力。尽管近年来该任务在设备泛化方面取得了显著进展，但不同地理区域间涉及时间、空间、文化及语言差异的领域转移挑战目前仍缺乏充分研究。此外，考虑到现实世界中存在大量未标注声学场景数据，探索利用这些未标注数据的可行方法具有重要意义。为此，我们在ICME 2024大挑战赛中提出"领域转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，旨在开发更鲁棒的领域转移场景下的声学场景分类模型。