A Multimodal Symphony: Integrating Taste and Sound through Generative AI
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909758984814592 |
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| author | Spanio, Matteo Zampini, Massimiliano Rodà, Antonio Pierucci, Franco |
| author_facet | Spanio, Matteo Zampini, Massimiliano Rodà, Antonio Pierucci, Franco |
| contents | In recent decades, neuroscientific and psychological research has traced direct relationships between taste and auditory perceptions. This article explores multimodal generative models capable of converting taste information into music, building on this foundational research. We provide a brief review of the state of the art in this field, highlighting key findings and methodologies. We present an experiment in which a fine-tuned version of a generative music model (MusicGEN) is used to generate music based on detailed taste descriptions provided for each musical piece. The results are promising: according the participants' ($n=111$) evaluation, the fine-tuned model produces music that more coherently reflects the input taste descriptions compared to the non-fine-tuned model. This study represents a significant step towards understanding and developing embodied interactions between AI, sound, and taste, opening new possibilities in the field of generative AI. We release our dataset, code and pre-trained model at: https://osf.io/xs5jy/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02823 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multimodal Symphony: Integrating Taste and Sound through Generative AI Spanio, Matteo Zampini, Massimiliano Rodà, Antonio Pierucci, Franco Sound Artificial Intelligence Multimedia Audio and Speech Processing I.2.6; J.5 In recent decades, neuroscientific and psychological research has traced direct relationships between taste and auditory perceptions. This article explores multimodal generative models capable of converting taste information into music, building on this foundational research. We provide a brief review of the state of the art in this field, highlighting key findings and methodologies. We present an experiment in which a fine-tuned version of a generative music model (MusicGEN) is used to generate music based on detailed taste descriptions provided for each musical piece. The results are promising: according the participants' ($n=111$) evaluation, the fine-tuned model produces music that more coherently reflects the input taste descriptions compared to the non-fine-tuned model. This study represents a significant step towards understanding and developing embodied interactions between AI, sound, and taste, opening new possibilities in the field of generative AI. We release our dataset, code and pre-trained model at: https://osf.io/xs5jy/. |
| title | A Multimodal Symphony: Integrating Taste and Sound through Generative AI |
| topic | Sound Artificial Intelligence Multimedia Audio and Speech Processing I.2.6; J.5 |
| url | https://arxiv.org/abs/2503.02823 |