AI-Based Melody Generation

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.

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Publication Details

Published
2025-01-10
DOI
https://doi.org/10.1109/ccnc54725.2025.10975980
Citations
1
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
1.49
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article

AI-Based Melody Generation

Roberto Cavicchioli, Marco Furini, Jia Cheng Hu
1 citations
Music and Audio Processing
1.49
article

AI-Based Melody Generation

Roberto Cavicchioli, Marco Furini, Jia Cheng Hu
article en
1 citations

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.

University of Modena and Reggio Emilia (IT)
Openalex Percentile: Top 20%
Music and Audio Processing
1.49
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AI-Based Melody Generation — Roberto Cavicchioli, Marco Furini, et al. · (2025) | TGRS Research Map | TGRS