Decoding Musical Origins: Distinguishing Human and AI Composers

• URL: http://arxiv.org/abs/2509.11369v1
• Date: Sun, 14 Sep 2025 17:50:33 GMT
• Title: Decoding Musical Origins: Distinguishing Human and AI Composers
• Authors: Cheng-Yang Tsai, Tzu-Wei Huang, Shao-Yu Wei, Guan-Wei Chen, Hung-Ying Chu, Yu-Cheng Lin,
• Abstract summary: YNote is a novel, machine-learning-friendly music notation system.<n>We train an effective classification model capable of distinguishing whether a piece of music was composed by a human.<n>The model achieves an accuracy of 98.25%, successfully demonstrating that YNote retains sufficient stylistic information.
• Score: 0.6246322794612152
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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