SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval
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Computer Science > Sound
Title:SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval
Abstract:Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low semantic diversity, generic descriptions lacking acoustic details, and one-to-one audio-caption mappings that poorly reflect the inherent ambiguity of auditory perception. We introduce SonicCaps, a large-scale audio captioning dataset comprising ~15M captions paired with ~700k audio clips, generated using a multi-modal large language model (Qwen3-Omni) conditioned on both audio and text. To explicitly promote diversity, we generate around 24 captions per audio via structured prompt engineering and few- shot generation, spanning main descriptions, rephrased variants (verbosity, style) and semantic tags. Human evaluation shows that SonicCaps is rated significantly higher than existing captioning datasets, with fine-grained analyses indicating that our captions are perceived as more descriptive and precise, which strongly correlates with quality judgments. Finally, training CLAP models on SonicCaps with a multi-caption sampling strategy consistently improves audio retrieval and zero-shot classification, with stronger generalization across public and commercial benchmarks. We release both SonicCaps and two specialized CLAP models on hugging face: this https URL.
| Subjects: | Sound (cs.SD); Computation and Language (cs.CL); Multimedia (cs.MM); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2609.02343 [cs.SD] |
| (or arXiv:2609.02343v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2609.02343
arXiv-issued DOI via DataCite (pending registration)
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