Abstract
Concept-based interpretability methods like TCAV require clean, well-separated positive and negative examples for each concept. Existing music datasets lack this structure: tags are sparse, noisy, or ill-defined. We introduce ConceptCaps, a dataset of 23k music-caption-audio triplets with explicit labels from a 200-attribute taxonomy. Our pipeline separates semantic modeling from text generation: a VAE learns plausible attribute co-occurrence patterns, a fine-tuned LLM converts attribute lists into professional descriptions, and MusicGen synthesizes corresponding audio. This separation improves coherence and controllability over end-to-end approaches. We validate the dataset through audio-text alignment (CLAP), linguistic quality metrics (BERTScore, MAUVE), and TCAV analysis confirming that concept probes recover musically meaningful patterns. Dataset and code are available online.
Abstract (translated)
基于概念的可解释性方法（如TCAV）需要每个概念具备清晰、分离良好的正例和反例。现有的音乐数据集缺乏这种结构：标签稀疏、嘈杂或定义不清。为此，我们引入了ConceptCaps数据集，它包含23,000个音乐-描述-音频三元组，并附有从一个包含200个属性的分类法中明确标注的标签。我们的流水线将语义建模与文本生成分离：VAE（变分自编码器）学习合理的属性共现模式，微调后的LLM（大规模语言模型）将属性列表转换为专业描述，而MusicGen则合成相应的音频。这种分离提高了连贯性和可控制性，优于端到端的方法。我们通过音频-文本对齐（CLAP）、语言质量指标（BERTScore、MAUVE）以及TCAV分析验证了该数据集的有效性，后者确认概念探测可以恢复音乐上有意义的模式。数据集和代码在线提供。
URL
https://arxiv.org/abs/2601.14157
