Story2MIDI: Emotionally Aligned Music Generation from Text
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918227554074624 |
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| author | Shokri, Mohammad Salem, Alexandra C. Levine, Gabriel Devaney, Johanna Levitan, Sarah Ita |
| author_facet | Shokri, Mohammad Salem, Alexandra C. Levine, Gabriel Devaney, Johanna Levitan, Sarah Ita |
| contents | In this paper, we introduce Story2MIDI, a sequence-to-sequence Transformer-based model for generating emotion-aligned music from a given piece of text. To develop this model, we construct the Story2MIDI dataset by merging existing datasets for sentiment analysis from text and emotion classification in music. The resulting dataset contains pairs of text blurbs and music pieces that evoke the same emotions in the reader or listener. Despite the small scale of our dataset and limited computational resources, our results indicate that our model effectively learns emotion-relevant features in music and incorporates them into its generation process, producing samples with diverse emotional responses. We evaluate the generated outputs using objective musical metrics and a human listening study, confirming the model's ability to capture intended emotional cues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_02192 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Story2MIDI: Emotionally Aligned Music Generation from Text Shokri, Mohammad Salem, Alexandra C. Levine, Gabriel Devaney, Johanna Levitan, Sarah Ita Sound Artificial Intelligence Computation and Language In this paper, we introduce Story2MIDI, a sequence-to-sequence Transformer-based model for generating emotion-aligned music from a given piece of text. To develop this model, we construct the Story2MIDI dataset by merging existing datasets for sentiment analysis from text and emotion classification in music. The resulting dataset contains pairs of text blurbs and music pieces that evoke the same emotions in the reader or listener. Despite the small scale of our dataset and limited computational resources, our results indicate that our model effectively learns emotion-relevant features in music and incorporates them into its generation process, producing samples with diverse emotional responses. We evaluate the generated outputs using objective musical metrics and a human listening study, confirming the model's ability to capture intended emotional cues. |
| title | Story2MIDI: Emotionally Aligned Music Generation from Text |
| topic | Sound Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2512.02192 |