Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917401476464640 |
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| author | Spanio, Matteo Frezzato, Valentina Rodà, Antonio |
| author_facet | Spanio, Matteo Frezzato, Valentina Rodà, Antonio |
| contents | Collecting large, aligned cross-modal datasets for music-flavor research is difficult because perceptual experiments are costly and small by design. We address this bottleneck through two complementary experiments. The first tests whether audio-flavor correlations, feature-importance rankings, and latent-factor structure transfer from an experimental soundtracks collection (257~tracks with human annotations) to a large FMA-derived corpus ($\sim$49,300 segments with synthetic labels). The second validates computational flavor targets -- derived from food chemistry via a reproducible pipeline -- against human perception in an online listener study (49~participants, 20~tracks). Results from both experiments converge: the quantitative transfer analysis confirms that cross-modal structure is preserved across supervision regimes, and the perceptual evaluation shows significant alignment between computational targets and listener ratings (permutation $p<0.0001$, Mantel $r=0.45$, Procrustes $m^2=0.51$). Together, these findings support the conclusion that sonic seasoning effects are present in synthetic FMA annotations. We release datasets and companion code to support reproducible cross-modal AI research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10632 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences Spanio, Matteo Frezzato, Valentina Rodà, Antonio Sound Machine Learning Multimedia Audio and Speech Processing Collecting large, aligned cross-modal datasets for music-flavor research is difficult because perceptual experiments are costly and small by design. We address this bottleneck through two complementary experiments. The first tests whether audio-flavor correlations, feature-importance rankings, and latent-factor structure transfer from an experimental soundtracks collection (257~tracks with human annotations) to a large FMA-derived corpus ($\sim$49,300 segments with synthetic labels). The second validates computational flavor targets -- derived from food chemistry via a reproducible pipeline -- against human perception in an online listener study (49~participants, 20~tracks). Results from both experiments converge: the quantitative transfer analysis confirms that cross-modal structure is preserved across supervision regimes, and the perceptual evaluation shows significant alignment between computational targets and listener ratings (permutation $p<0.0001$, Mantel $r=0.45$, Procrustes $m^2=0.51$). Together, these findings support the conclusion that sonic seasoning effects are present in synthetic FMA annotations. We release datasets and companion code to support reproducible cross-modal AI research. |
| title | Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences |
| topic | Sound Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2604.10632 |