In the evolving realm of social media, music significantly enhances post appeal and viewer engagement. However, challenges such as copyright and royalties complicate its usage. Artificial intelligence (AI) might be used to generate royalty-free music that complements visual content. This paper presents an AI-based melody generator designed to create music suitable for various applications, particularly for enhancing social media posts. Unlike full songs, which involve complex AI models, our focus on melodies addresses a more specific and manageable aspect of music generation. We developed an algorithm to differentiate between main and background melodies, leveraging an LSTM and Transformer architecture to capture musical dependencies. Training on the Lakn MIDI dataset, which includes 178,000 files, our model achieved 64% accuracy in predicting main melodies and 78% in background melodies. Evaluation by 23 volunteers revealed that AI-generated melodies were as pleasant as human-composed ones and revealed that participants struggled to distinguish whether the melody they heard was human-composed or AI-generated. This indicates that our AI model might offer significant benefits in scenarios where melodies play an important role.
AI-Based Melody Generation / Cavicchioli, R.; Hu, Jia Cheng; Furini, M.. - (2025), pp. 01-06. ( 22nd IEEE Consumer Communications and Networking Conference, CCNC 2025 Las Vegas 10-13 Gennaio 2025) [10.1109/CCNC54725.2025.10975980].
AI-Based Melody Generation
Cavicchioli R.;Hu J. C.;Furini M.
2025
Abstract
In the evolving realm of social media, music significantly enhances post appeal and viewer engagement. However, challenges such as copyright and royalties complicate its usage. Artificial intelligence (AI) might be used to generate royalty-free music that complements visual content. This paper presents an AI-based melody generator designed to create music suitable for various applications, particularly for enhancing social media posts. Unlike full songs, which involve complex AI models, our focus on melodies addresses a more specific and manageable aspect of music generation. We developed an algorithm to differentiate between main and background melodies, leveraging an LSTM and Transformer architecture to capture musical dependencies. Training on the Lakn MIDI dataset, which includes 178,000 files, our model achieved 64% accuracy in predicting main melodies and 78% in background melodies. Evaluation by 23 volunteers revealed that AI-generated melodies were as pleasant as human-composed ones and revealed that participants struggled to distinguish whether the melody they heard was human-composed or AI-generated. This indicates that our AI model might offer significant benefits in scenarios where melodies play an important role.Pubblicazioni consigliate

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