SsgCaps：用于评估声音场景生成算法的受控数据集
SsgCaps: A controlled dataset for the evaluation of sound scene generation algorithms
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中文总结 AI 辅助
本文提出SsgCaps，一个基于动作类型学提示的公开声音场景数据集，仅含公共领域音频，并通过FAD和KAD等指标验证其与原始版本差异小，适合用于SSG算法基准测试。
中文摘要 AI 辅助
声音场景生成涉及人工声音场景的自动合成。我们引入了SsgCaps，一个公开可用的人工设计声音场景数据集，其中每个场景都匹配一个精确结构的提示，该提示指导采样过程。相应的提示是从一个预定义的基于动作的类型学中采样，该类型学允许在保持合理性的同时进行广泛采样。SsgCaps是一个声音场景数据集，源自2024年DCASE挑战赛第7任务中未发表的参考数据集，该数据集包含私有和公共领域的音频样本。相比之下，SsgCaps仅包含公共领域的音频样本，使我们能够向社区开放此数据集。为了使该数据集对社区有用，我们首先详细阐述了提示和数据集结构的原理。然后，我们对数据集的2个版本进行了比较定量分析。为此，我们将两个版本与提交给挑战赛的SSG算法合成的音频进行比较，使用Fr{é}chet音频距离（FAD）和核音频距离（KAD）以及感知评级。该分析显示差异很小，这使我们能够推荐开放版本用于SSG算法的进一步基准测试。
英文摘要
Sound Scene Generation is about the automatic synthesis of artificial sound scenes. We introduce SsgCaps, a publicly available dataset of human-engineered sound scenes wherein each scene matches a precisely structured prompt that guides the sampling process. The corresponding prompts are sampled from a predefined action-based typology that allows extensive sampling while retaining plausibility. SsgCaps is a sound scene dataset derived from the unpublished reference dataset for Task 7 of the 2024 DCASE Challenge edition, which contained private-and public-domain audio samples. In contrast, SsgCaps contains only public-domain audio samples, allowing us to open this dataset to the community. To make this dataset useful to the community, we first elaborate on the rationale for the prompt and dataset structure. We then perform a comparative quantitative analysis of the 2 versions of the dataset. To do so, we compare both versions to the audio synthesized by the SSG algorithms submitted to the challenge using Fr{é}chet Audio Distance (FAD) and Kernel Audio Distance (KAD) as well as perceptual ratings. This analysis shows only small differences, which enables us to recommend the open version for further benchmarking of SSG algorithms.
