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JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata

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arxiv 2502.07461 v2 pith:DB45ZUNP submitted 2025-02-11 cs.SD cs.AI

classification cs.SDcs.AI
keywords datasetmetadatajamendomaxcapsapproachimputedintroducelargemodel
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We introduce JamendoMaxCaps, a large-scale music-caption dataset featuring over 362,000 freely licensed instrumental tracks from the renowned Jamendo platform. The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata. We also introduce a retrieval system that leverages both musical features and metadata to identify similar songs, which are then used to fill in missing metadata using a local large language model (LLLM). This approach allows us to provide a more comprehensive and informative dataset for researchers working on music-language understanding tasks. We validate this approach quantitatively with five different measurements. By making the JamendoMaxCaps dataset publicly available, we provide a high-quality resource to advance research in music-language understanding tasks such as music retrieval, multimodal representation learning, and generative music models.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Revisiting Audio-language Pretraining for Learning General-purpose Audio Representation

    eess.AS 2025-11 conditional novelty 6.0 of 10

    A 10.7M-pair audio-caption corpus and systematic comparison show contrastive pretraining is more data-efficient while captioning scales better, and supervised initialization yields diminishing returns.

  2. Auto-Regressive vs Flow-Matching: a Comparative Study of Modeling Paradigms for Text-to-Music Generation

    cs.SD 2025-06 conditional novelty 6.0 of 10

    Under matched training conditions, auto-regressive models slightly outperform flow-matching on music quality and temporal control, while flow-matching offers faster inference and better inpainting flexibility.

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