Decoding Musical Origins: Distinguishing Human and AI Composers
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916949189984256 |
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| author | Tsai, Cheng-Yang Huang, Tzu-Wei Wei, Shao-Yu Chen, Guan-Wei Chu, Hung-Ying Lin, Yu-Cheng |
| author_facet | Tsai, Cheng-Yang Huang, Tzu-Wei Wei, Shao-Yu Chen, Guan-Wei Chu, Hung-Ying Lin, Yu-Cheng |
| contents | With the rapid advancement of Large Language Models (LLMs), AI-driven music generation has become a vibrant and fruitful area of research. However, the representation of musical data remains a significant challenge. To address this, a novel, machine-learning-friendly music notation system, YNote, was developed. This study leverages YNote to train an effective classification model capable of distinguishing whether a piece of music was composed by a human (Native), a rule-based algorithm (Algorithm Generated), or an LLM (LLM Generated). We frame this as a text classification problem, applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to extract structural features from YNote sequences and using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The resulting model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information for analysis. More importantly, the model can identify the unique " technological fingerprints " left by different AI generation techniques, providing a powerful tool for tracing the origins of AI-generated content. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding Musical Origins: Distinguishing Human and AI Composers Tsai, Cheng-Yang Huang, Tzu-Wei Wei, Shao-Yu Chen, Guan-Wei Chu, Hung-Ying Lin, Yu-Cheng Machine Learning With the rapid advancement of Large Language Models (LLMs), AI-driven music generation has become a vibrant and fruitful area of research. However, the representation of musical data remains a significant challenge. To address this, a novel, machine-learning-friendly music notation system, YNote, was developed. This study leverages YNote to train an effective classification model capable of distinguishing whether a piece of music was composed by a human (Native), a rule-based algorithm (Algorithm Generated), or an LLM (LLM Generated). We frame this as a text classification problem, applying the Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to extract structural features from YNote sequences and using the Synthetic Minority Over-sampling Technique (SMOTE) to address data imbalance. The resulting model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information for analysis. More importantly, the model can identify the unique " technological fingerprints " left by different AI generation techniques, providing a powerful tool for tracing the origins of AI-generated content. |
| title | Decoding Musical Origins: Distinguishing Human and AI Composers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.11369 |