Music4All A+A Article Swipe
YOU?
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· 2025
· Open Access
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· DOI: https://doi.org/10.5281/zenodo.17278676
· OA: W4417077609
Music4All A+A: Artist and Album Dataset Music4All A+A (Artist and Album) is a large-scale multimodal dataset for Music Information Retrieval (MIR) tasks, providing comprehensive metadata, genre labels, image representations, and textual descriptors for 6,741 artists and 19,511 albums. This dataset extends the [Music4All-Onion dataset](https://doi.org/10.1145/3511808.3557656) by providing multimodal data at the artist and album level, enabling research in:- Multimodal music genre classification- Music recommendation systems- Missing-modality scenarios- Cross-domain transfer learning Key Features - Multimodal Data: Images and text for both artists and albums- Rich Genre Labels: 659 unique artist genres and 737 album genres- Balanced Distribution: Addresses class imbalance issues in existing datasets- Missing-Modality Splits: Pre-defined test splits for evaluating robustness (10%, 30%, 50%, 70%, 90%, 100% modality availability)- Extensible: Built on Music4All-Onion, allowing integration with track-level audio, video, and user-item interaction data Note: The missing-modality splits are nested, meaning that items in the 10% subset are also present in 30%, 50%, etc. Citation: If you use this dataset in your research, please cite: @inproceedings{geiger2025music4all, title={Music4All A+A: A Multimodal Dataset for Music Information Retrieval Tasks}, author={Geiger, Jonas and Moscati, Marta and Nawaz, Shah and Schedl, Markus}, booktitle={Proceedings of the IEEE International Conference on Content-Based Multimedia Indexing, Dublin, Ireland, October 22-24, 2025}, year={2025}, url={https://arxiv.org/abs/2509.14891}}
